{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating random NN models with different baseline structures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2019.11.1\n",
    "Stephen Wu\n",
    "\n",
    "Goal of this script: \n",
    "sample codes of generating random neural network models based on a few different baseline structures other than the basic ones shown in the XenonPy official tutorial.\n",
    "    1. fixing the range of number of neurons in the first and last hidden layer with linear reduction of the range from the first to the last layer.\n",
    "    2. fixing the range of number of neurons in the first and last hidden layer with log reduction of the range from the first to the last layer.\n",
    "    3. randomly picking a subset of descriptors from the full descriptors as input for each neural network\n",
    "    \n",
    "Data from Pubchem (a subset selected by Ikebata) is used as an example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle as pk\n",
    "\n",
    "# user-friendly print\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Load files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can download the in-house data at https://github.com/yoshida-lab/XenonPy/releases/download/v0.4.1/iQSPR_sample_data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0', 'SMILES', 'E', 'HOMO-LUMO gap'], dtype='object')\n",
      "(16674, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>E</th>\n",
       "      <th>HOMO-LUMO gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>CC(=O)OC(CC(=O)[O-])C[N+](C)(C)C</td>\n",
       "      <td>177.577</td>\n",
       "      <td>4.352512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>CC(=O)OC(CC(=O)O)C[N+](C)(C)C</td>\n",
       "      <td>185.735</td>\n",
       "      <td>7.513497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>C1=CC(C(C(=C1)C(=O)O)O)O</td>\n",
       "      <td>98.605</td>\n",
       "      <td>4.581348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>CC(CN)O</td>\n",
       "      <td>83.445</td>\n",
       "      <td>8.034841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>C(C(=O)COP(=O)(O)O)N</td>\n",
       "      <td>90.877</td>\n",
       "      <td>5.741310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                            SMILES        E  HOMO-LUMO gap\n",
       "0           1  CC(=O)OC(CC(=O)[O-])C[N+](C)(C)C  177.577       4.352512\n",
       "1           2     CC(=O)OC(CC(=O)O)C[N+](C)(C)C  185.735       7.513497\n",
       "2           3          C1=CC(C(C(=C1)C(=O)O)O)O   98.605       4.581348\n",
       "3           4                           CC(CN)O   83.445       8.034841\n",
       "4           5              C(C(=O)COP(=O)(O)O)N   90.877       5.741310"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load QM9 data from csv file\n",
    "data = pd.read_csv('./iQSPR_sample_data.csv')\n",
    "\n",
    "# take a look at the data\n",
    "print(data.columns)\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Fingerprints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xenonpy.descriptor import Fingerprints\n",
    "\n",
    "FPs_ECFP_MACCS = Fingerprints(featurizers=['ECFP','MACCS'], input_type='smiles', on_errors='nan')\n",
    "FPs_All = Fingerprints(featurizers=['RDKitFP','AtomPairFP','TopologicalTorsionFP','ECFP','FCFP','MACCS'], input_type='smiles', on_errors='nan')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train DNN models\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. fixing the range of number of neurons in the first and last hidden layer with linear reduction of the range from the first to the last layer.\n",
    "##### 2. fixing the range of number of neurons in the first and last hidden layer with log reduction of the range from the first to the last layer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "roughly takes 1min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.18 s, sys: 764 ms, total: 9.94 s\n",
      "Wall time: 16.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# pick a property as output\n",
    "prop = data['HOMO-LUMO gap'].to_frame()\n",
    "# pre-calculate all descriptors\n",
    "desc = FPs_ECFP_MACCS.transform(data['SMILES'])\n",
    "# remove NaN row\n",
    "prop = prop[~desc.isnull().any(axis=1)]\n",
    "desc = desc[~desc.isnull().any(axis=1)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "prepare a function to generate random number of neurons uniformly from a given range at each hidden layer for a given total number of layer, for which the max and min values of the ranges are linearly decaying with a pair of fixed max and min values at the first and the last hidden layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the fixed max and min values at the first and the last hidden layer \n",
    "# are determined empirically based on the length of the descriptor\n",
    "\n",
    "def neuron_vector_lin(nL):\n",
    "    max_vec = np.linspace(1500,100,nL)\n",
    "    min_vec = np.linspace(1000,20,nL)\n",
    "    return sorted([np.random.randint(min_vec[i], max_vec[i]) for i in range(nL)], reverse=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1201, 933, 564, 274, 89]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1113, 939, 682, 291, 70]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1121, 1008, 590, 448, 30]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1104, 1041, 792, 436, 66]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1235, 974, 793, 298, 65]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1433, 885, 534, 424, 89]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1319, 787, 595, 400, 32]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1476, 846, 799, 316, 56]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1019, 1015, 628, 397, 69]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1353, 871, 513, 307, 43]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test the function\n",
    "for _ in range(10):\n",
    "    neuron_vector_lin(5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "prepare a function to generate random number of neurons uniformly from a given range at each hidden layer for a given total number of layer, for which the max and min values of the ranges are decaying in log-scale with a pair of fixed max and min values at the first and the last hidden layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the fixed max and min values at the first and the last hidden layer \n",
    "# are determined empirically based on the length of the descriptor\n",
    "\n",
    "def neuron_vector_log(nL):\n",
    "    log_base = 2\n",
    "    max_vec = np.round(np.logspace(np.log2(100),np.log2(1500),nL,base=log_base))\n",
    "    min_vec = np.round(np.logspace(np.log2(20),np.log2(1000),nL,base=log_base))\n",
    "    return sorted([np.random.randint(min_vec[i], max_vec[i]) for i in range(nL)], reverse=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1454, 723, 255, 101, 41]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1142, 617, 257, 80, 80]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1235, 513, 299, 160, 85]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1397, 540, 184, 183, 74]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1188, 410, 193, 127, 81]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1109, 669, 358, 115, 42]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1475, 467, 325, 119, 24]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1018, 565, 232, 139, 62]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1419, 524, 263, 85, 61]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[1180, 731, 212, 151, 79]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test the function\n",
    "for _ in range(10):\n",
    "    neuron_vector_log(5)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "prepare a generator with a selected \"neuron_vector\" function (we use LeakyReLu in this example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn import LeakyReLU\n",
    "from xenonpy.utils import ParameterGenerator\n",
    "\n",
    "generator = ParameterGenerator(\n",
    "    in_features=desc.shape[1],\n",
    "    out_features=1,\n",
    "    h_neurons=dict(\n",
    "        data=neuron_vector_lin, \n",
    "        repeat=(3, 4, 5)\n",
    "    ),\n",
    "    h_activation_funcs=(LeakyReLU(),)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'in_features': 2215, 'out_features': 1, 'h_activation_funcs': LeakyReLU(negative_slope=0.01), 'h_neurons': (1106, 944, 457, 57)}\n",
      "{'in_features': 2215, 'out_features': 1, 'h_activation_funcs': LeakyReLU(negative_slope=0.01), 'h_neurons': (1092, 1016, 483, 91)}\n",
      "{'in_features': 2215, 'out_features': 1, 'h_activation_funcs': LeakyReLU(negative_slope=0.01), 'h_neurons': (1365, 739, 401, 42)}\n",
      "{'in_features': 2215, 'out_features': 1, 'h_activation_funcs': LeakyReLU(negative_slope=0.01), 'h_neurons': (1243, 846, 616, 439, 73)}\n",
      "{'in_features': 2215, 'out_features': 1, 'h_activation_funcs': LeakyReLU(negative_slope=0.01), 'h_neurons': (1433, 1071, 611, 287, 32)}\n"
     ]
    }
   ],
   "source": [
    "# test the function\n",
    "\n",
    "for parameters in generator(num=5):\n",
    "    print(parameters)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "prepare a function to automatically generate names for each model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_name(model):\n",
    "    name = ['HLgap']\n",
    "    for n, m in model.named_children():\n",
    "        if 'layer_' in n:\n",
    "            name.append(str(m.linear.in_features))\n",
    "        else:\n",
    "            name.append(str(m.in_features))\n",
    "            name.append(str(m.out_features))\n",
    "    return '-'.join(name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HLgap-2215-1272-917-651-278-77-1\n",
      "HLgap-2215-1026-902-507-49-1\n",
      "HLgap-2215-1320-825-759-305-55-1\n"
     ]
    }
   ],
   "source": [
    "# test the function\n",
    "from xenonpy.model import SequentialLinear, LinearLayer\n",
    "\n",
    "for paras, model in generator(num=3, factory=SequentialLinear):\n",
    "    print(make_name(model))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "training models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "from xenonpy.datatools import preset, Splitter\n",
    "from xenonpy.descriptor import Compositions\n",
    "\n",
    "from xenonpy.model import SequentialLinear, LinearLayer\n",
    "from xenonpy.model.training import Trainer, SGD, MSELoss, Adam, ReduceLROnPlateau, ExponentialLR, ClipValue\n",
    "from xenonpy.model.training.extension import Validator, TensorConverter, Persist\n",
    "from xenonpy.model.training.dataset import ArrayDataset\n",
    "from xenonpy.model.utils import regression_metrics\n",
    "\n",
    "from collections import OrderedDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare a trainer\n",
    "trainer = Trainer(\n",
    "    optimizer=Adam(lr=0.01),\n",
    "    loss_func=MSELoss(),\n",
    "    cuda=False,\n",
    ").extend(\n",
    "    TensorConverter(),\n",
    "    Validator(metrics_func=regression_metrics, early_stopping=50, trace_order=3, mae=0.0, pearsonr=1.0),\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:   8%|▊         | 15/200 [02:03<25:27,  8.26s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 391\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:  12%|█▎        | 25/200 [03:01<21:08,  7.25s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 664\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:   4%|▍         | 8/200 [00:55<22:16,  6.96s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 197\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:   5%|▌         | 10/200 [01:07<21:22,  6.75s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 262\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:  10%|▉         | 19/200 [03:29<33:17, 11.04s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 508\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:  12%|█▏        | 23/200 [03:15<25:05,  8.51s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 610\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:   6%|▌         | 11/200 [01:43<29:32,  9.38s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 291\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:   4%|▎         | 7/200 [00:54<25:13,  7.84s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 179\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 562\n"
     ]
    },
    {
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    }
   ],
   "source": [
    "summary = []\n",
    "save_folder = 'Test_HLgap_MACCSECFP'\n",
    "N_train = int(prop.shape[0]*0.8)\n",
    "train_batch_size = 512 # based on total number data\n",
    "test_batch_size = 1024 # based on total number data\n",
    "max_epochs = 200\n",
    "\n",
    "for paras, model in generator(num=100, factory=SequentialLinear):\n",
    "    sp = Splitter(prop.shape[0], test_size=prop.shape[0]-N_train)\n",
    "    x_train, x_val, y_train, y_val = sp.split(desc, prop)\n",
    "    \n",
    "    train_dataset = DataLoader(ArrayDataset(x_train, y_train), shuffle=True, batch_size=train_batch_size)\n",
    "    val_dataset = DataLoader(ArrayDataset(x_val, y_val), batch_size=test_batch_size)\n",
    "    \n",
    "    model_name = make_name(model)\n",
    "    persist = Persist(\n",
    "        f'{save_folder}/{model_name}', \n",
    "        # -^- required -^-\n",
    "        \n",
    "        # -v- optional -v-\n",
    "        increment=True, \n",
    "        sync_training_step=True,\n",
    "        model_class=SequentialLinear,\n",
    "        model_params=paras,\n",
    "        author='Someone',\n",
    "        email='someone@email.com',\n",
    "        dataset='Pubchem_ikebata',\n",
    "    )\n",
    "    _ = trainer.extend(persist)\n",
    "    trainer.reset(to=model)\n",
    "    \n",
    "    trainer.fit(training_dataset=train_dataset, validation_dataset=val_dataset, epochs=max_epochs)\n",
    "    persist(splitter=sp, data_indices=prop.index.tolist())  # <-- calling of this method only after the model training\n",
    "    \n",
    "    training_info = trainer.training_info\n",
    "    \n",
    "    summary.append(OrderedDict(\n",
    "        id=model_name,\n",
    "        mae=training_info['val_mae'].min(),\n",
    "        mse=training_info['val_mse'].min(),\n",
    "        r2=training_info['val_r2'].max(),\n",
    "        corr=training_info['val_pearsonr'].max(),\n",
    "        spearman_corr=training_info['val_spearmanr'].max(),\n",
    "    ))\n",
    "    \n",
    "# record the summary of the results\n",
    "summary = pd.DataFrame(summary)\n",
    "summary.to_pickle(f'{save_folder}/summary.pd.xz')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot training results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare target and plotting folders, and load models\n",
    "import os\n",
    "from xenonpy.model.training import Checker\n",
    "\n",
    "target_folder = 'Test_HLgap_MACCSECFP'\n",
    "save_plots = 'Plots_' + target_folder\n",
    "\n",
    "if not os.path.exists(save_plots):\n",
    "    os.makedirs(save_plots)\n",
    "\n",
    "checkers = []\n",
    "model_list = []\n",
    "tmp_file = os.listdir(target_folder)\n",
    "for tmp in tmp_file:\n",
    "    if os.path.isdir(f'{target_folder}/{tmp}'):\n",
    "        model_list.append(tmp)\n",
    "        checkers.append(Checker(f'{target_folder}/{tmp}'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction vs. observation plots (single test trial)\n",
    "import seaborn as sb\n",
    "\n",
    "def draw(y_true, y_pred, y_true_fit=None, y_pred_fit=None, *, prop_name, log_scale=False, file_dir=None, file_name=None):\n",
    "\n",
    "    mask = ~np.isnan(y_pred)\n",
    "    y_true = y_true[mask]\n",
    "    y_pred = y_pred[mask]\n",
    "        \n",
    "    data = pd.DataFrame(dict(Observation=y_true, Prediction=y_pred, dataset=['test'] * len(y_true)))\n",
    "    scores = metrics(data['Observation'], data['Prediction'])\n",
    "    \n",
    "    if y_true_fit is not None and y_pred_fit is not None:\n",
    "        mask = ~np.isnan(y_pred_fit)\n",
    "        y_true_fit = y_true_fit[mask]\n",
    "        y_pred_fit = y_pred_fit[mask]\n",
    "        train_ = pd.DataFrame(dict(Observation=y_true_fit, Prediction=y_pred_fit, dataset=['train'] * len(y_true_fit)))\n",
    "        data = pd.concat([train_, data])\n",
    "\n",
    "    if log_scale:\n",
    "        data = data.apply(lambda c: np.log(c.values) if c.dtype.type is not np.object_ else c, axis=0)\n",
    "#         test_ = test_.apply(lambda c: np.log(c.values) if c.dtype.type is not np.object_ else c, axis=0)\n",
    "\n",
    "\n",
    "\n",
    "#     with sb.set(font_scale=2.5):\n",
    "    g = sb.lmplot(x=\"Prediction\", y=\"Observation\", hue=\"dataset\", ci=None,\n",
    "                  data=data, palette=\"Set1\", height=10, legend=False,  markers=[\".\", \"o\"],\n",
    "                  scatter_kws={'s': 25, 'alpha': 0.7}, hue_order=['train', 'test'])\n",
    "    \n",
    "    ax = plt.gca()\n",
    "    tmp = [data[\"Prediction\"].max(), data[\"Prediction\"].min(), data[\"Observation\"].max(), data[\"Observation\"].max()]\n",
    "    min_, max_ = np.min(tmp), np.max(tmp)\n",
    "    margin = (max_- min_) / 15\n",
    "    min_ = min_ - margin\n",
    "    max_ = max_ + margin\n",
    "    ax.set_xlim(min_, max_)\n",
    "    ax.set_ylim(min_, max_)\n",
    "    ax.set_xlabel(ax.get_xlabel(), fontsize='xx-large')\n",
    "    ax.set_ylabel(ax.get_ylabel(), fontsize='xx-large')\n",
    "    ax.tick_params(axis='both', which='major', labelsize='xx-large')\n",
    "    ax.plot((min_, max_), (min_, max_), ':', color='gray')\n",
    "    ax.set_title(prop_name, fontsize='xx-large')\n",
    "    if log_scale:\n",
    "        ax.set_title(prop_name + ' (log scale)', fontsize='xx-large')\n",
    "    ax.text(0.98, 0.03,\n",
    "            'MAE: %.5f\\nRMSE: %.5f\\nPearsonR: %.5f\\nSpearmanR: %.5f' % (scores['mae'], scores['rmse'], scores['pearsonr'], scores['spearmanr']),\n",
    "            transform=ax.transAxes, horizontalalignment='right', fontsize='xx-large')\n",
    "\n",
    "    ax.legend(loc='upper left', markerscale=2, fancybox=True, shadow=True, frameon=True, facecolor='w', fontsize=18)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    if file_dir and file_name:\n",
    "        if log_scale:\n",
    "            plt.savefig(file_dir + '/' + file_name + '_log_scale.png', dpi=300, bbox_inches='tight')\n",
    "        else:\n",
    "            plt.savefig(file_dir + '/' + file_name + '.png', dip=300, bbox_inches='tight')\n",
    "    else:\n",
    "        print('Missing directory and/or file name information!')\n",
    "        \n",
    "# calculating basic statistics for predictions\n",
    "def metrics(y_true, y_pred, ignore_nan=True):\n",
    "    from sklearn.metrics import mean_absolute_error, r2_score, mean_squared_error\n",
    "    from scipy.stats import pearsonr, spearmanr\n",
    "    \n",
    "    if ignore_nan:\n",
    "        mask = ~np.isnan(y_pred)\n",
    "        y_true = y_true[mask]\n",
    "        y_pred = y_pred[mask]\n",
    "    \n",
    "    mae = mean_absolute_error(y_true, y_pred)\n",
    "    rmse = np.sqrt(mean_squared_error(y_true, y_pred))\n",
    "    r2 = r2_score(y_true, y_pred)\n",
    "    pr, p_val = pearsonr(y_true, y_pred)\n",
    "    sr, _ = spearmanr(y_true, y_pred)\n",
    "    return dict(\n",
    "        mae=mae,\n",
    "        rmse=rmse,\n",
    "        r2=r2,\n",
    "        pearsonr=pr,\n",
    "        spearmanr=sr,\n",
    "        p_value=p_val\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "size mismatch, m1: [1024 x 10407], m2: [567 x 502] at /Users/distiller/project/conda/conda-bld/pytorch_1556653464916/work/aten/src/TH/generic/THTensorMath.cpp:961",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-0e7482345d9b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0mval_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataLoader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mArrayDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_batch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m     \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheckpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'mae_1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m     \u001b[0my_fit_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_fit_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheckpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'mae_1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_fit_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_fit_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprop_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'HL gap'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfile_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msave_plots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'P2O_'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mmodel_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/xepy37_test/lib/python3.7/site-packages/xenonpy/utils/useful_cls.py\u001b[0m in \u001b[0;36mfn_\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    100\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m                     \u001b[0mrt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m                 \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/xepy37_test/lib/python3.7/site-packages/xenonpy/model/training/trainer.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x_in, y_true, dataset, checkpoint, **model_params)\u001b[0m\n\u001b[1;32m    581\u001b[0m             \u001b[0my_trues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mx_in\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 583\u001b[0;31m                 \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_in\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    584\u001b[0m                 \u001b[0my_preds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m                 \u001b[0my_trues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/xepy37_test/lib/python3.7/site-packages/xenonpy/model/training/trainer.py\u001b[0m in \u001b[0;36m_predict\u001b[0;34m(x_, y_)\u001b[0m\n\u001b[1;32m    561\u001b[0m                 \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m                 \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 563\u001b[0;31m                 \u001b[0my_p_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mmodel_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    564\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m                 \u001b[0my_p_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mmodel_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/xepy37_test/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/xepy37_test/lib/python3.7/site-packages/xenonpy/model/sequential.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_h_layers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf'layer_{i}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/xepy37_test/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/xepy37_test/lib/python3.7/site-packages/xenonpy/model/sequential.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m         \u001b[0m_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m             \u001b[0m_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_out\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/xepy37_test/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/xepy37_test/lib/python3.7/site-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mweak_script_method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/xepy37_test/lib/python3.7/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mlinear\u001b[0;34m(input, weight, bias)\u001b[0m\n\u001b[1;32m   1404\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mbias\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1405\u001b[0m         \u001b[0;31m# fused op is marginally faster\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1406\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddmm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1407\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1408\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: size mismatch, m1: [1024 x 10407], m2: [567 x 502] at /Users/distiller/project/conda/conda-bld/pytorch_1556653464916/work/aten/src/TH/generic/THTensorMath.cpp:961"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x750 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot training loss (based on the best mae among the three traced models)\n",
    "import matplotlib.pyplot as plt\n",
    "from xenonpy.model.training import Trainer\n",
    "from xenonpy.model.training.dataset import ArrayDataset\n",
    "from torch.utils.data import DataLoader\n",
    "from xenonpy.model.training.extension import TensorConverter\n",
    "\n",
    "train_batch_size = 512 # based on total number data\n",
    "test_batch_size = 1024 # based on total number data\n",
    "\n",
    "for i, checker in enumerate(checkers):\n",
    "    # plot training loss\n",
    "    fig, ax = plt.subplots(figsize=(10, 5), dpi=150)\n",
    "    trainer = Trainer.load(from_=checker).extend(TensorConverter())\n",
    "    _ = trainer.training_info.plot(y=['train_mse_loss', 'val_mse'], ax=ax)\n",
    "    fig.savefig(f'{save_plots}/Train_{model_list[i]}')\n",
    "    \n",
    "    # plot observation vs. prediction (based on the best mae among the three traced models)\n",
    "    sp = checker['splitter']\n",
    "    x_train, x_val, y_train, y_val = sp.split(desc, prop)\n",
    "    \n",
    "    train_dataset = DataLoader(ArrayDataset(x_train, y_train), shuffle=True, batch_size=train_batch_size)\n",
    "    val_dataset = DataLoader(ArrayDataset(x_val, y_val), batch_size=test_batch_size)\n",
    "    \n",
    "    y_pred, y_true = trainer.predict(dataset=val_dataset, checkpoint='mae_1')\n",
    "    y_fit_pred, y_fit_true = trainer.predict(dataset=train_dataset, checkpoint='mae_1')\n",
    "    draw(y_true, y_pred, y_fit_true, y_fit_pred, prop_name='HL gap',file_dir=save_plots, file_name='P2O_'+model_list[i])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3. randomly picking a subset of descriptors from the full descriptors as input for each neural network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "roughly takes 2-3mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 56.4 s, sys: 6.77 s, total: 1min 3s\n",
      "Wall time: 2min 20s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# pick a property as output\n",
    "prop = data['HOMO-LUMO gap'].to_frame()\n",
    "# pre-calculate all descriptors\n",
    "desc = FPs_All.transform(data['SMILES'])\n",
    "# remove NaN row\n",
    "prop = prop[~desc.isnull().any(axis=1)]\n",
    "desc = desc[~desc.isnull().any(axis=1)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "prepare a function to generate random number of neurons uniformly from a given range at each hidden layer for a given total number of layer, for which the max and min values of the ranges are linearly decaying with a pair of fixed max and min values at the first and the last hidden layer determined based on fractions of the input dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the fixed max and min ratio at the first and the last hidden layer \n",
    "# are determined empirically based on the length of the descriptor\n",
    "\n",
    "def neuron_vector(nL, in_neu):\n",
    "    max_vec = np.linspace(int(in_neu*0.9),100,nL)\n",
    "    min_vec = np.linspace(int(in_neu*0.7),10,nL)\n",
    "    return sorted([np.random.randint(min_vec[i], max_vec[i]) for i in range(nL)], reverse=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[502, 385, 247, 201, 31]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[454, 363, 280, 124, 56]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[458, 424, 229, 173, 96]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[423, 361, 282, 201, 62]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[486, 332, 312, 121, 30]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[529, 379, 278, 117, 13]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[471, 348, 226, 165, 13]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[470, 364, 280, 134, 83]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[480, 334, 245, 169, 37]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[520, 337, 263, 167, 52]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test the function\n",
    "for _ in range(10):\n",
    "    neuron_vector(5, 600)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "prepare a function to automatically generate names for each model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_name(model):\n",
    "    name = ['HLgap']\n",
    "    for n, m in model.named_children():\n",
    "        if 'layer_' in n:\n",
    "            name.append(str(m.linear.in_features))\n",
    "        else:\n",
    "            name.append(str(m.in_features))\n",
    "            name.append(str(m.out_features))\n",
    "    return '-'.join(name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "training models (this time the generator is greated inside the loop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "from xenonpy.datatools import preset, Splitter\n",
    "from xenonpy.descriptor import Compositions\n",
    "\n",
    "from torch.nn import LeakyReLU\n",
    "from xenonpy.utils import ParameterGenerator\n",
    "\n",
    "from xenonpy.model import SequentialLinear, LinearLayer\n",
    "from xenonpy.model.training import Trainer, SGD, MSELoss, Adam, ReduceLROnPlateau, ExponentialLR, ClipValue\n",
    "from xenonpy.model.training.extension import Validator, TensorConverter, Persist\n",
    "from xenonpy.model.training.dataset import ArrayDataset\n",
    "from xenonpy.model.utils import regression_metrics\n",
    "\n",
    "from collections import OrderedDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = Trainer(\n",
    "    optimizer=Adam(lr=0.01),\n",
    "    loss_func=MSELoss(),\n",
    "    cuda=False,\n",
    ").extend(\n",
    "    TensorConverter(),\n",
    "    Validator(metrics_func=regression_metrics, early_stopping=50, trace_order=3, mae=0.0, pearsonr=1.0),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Training:   0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of descriptors picked:  567\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:   4%|▍         | 20/500 [02:13<53:32,  6.69s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 536\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "summary = []\n",
    "save_folder = 'Test_HLgap_RandFP500to1k'\n",
    "N_train = int(prop.shape[0]*0.8)\n",
    "train_batch_size = 512 # based on total number data\n",
    "test_batch_size = 1024 # based on total number data\n",
    "max_epochs = 200\n",
    "N_desc_range = (500, 1000)\n",
    "\n",
    "for _ in range(100):\n",
    "    \n",
    "    sp = Splitter(prop.shape[0], test_size=prop.shape[0]-N_train)\n",
    "    x_train, x_val, y_train, y_val = sp.split(desc, prop)\n",
    "    \n",
    "    # remove all-0 or all-1 columns based on the training set\n",
    "    x_train = x_train.loc[:, (x_train != 0).any(axis=0)]\n",
    "    x_train = x_train.loc[:, (x_train != 1).any(axis=0)]\n",
    "    \n",
    "    # randomly pick a subset of descriptors\n",
    "    x_train = x_train.sample(min(np.random.randint(N_desc_range[0],N_desc_range[1]+1), x_train.shape[1]), axis=1)\n",
    "    x_val = x_val[x_train.columns]\n",
    "    \n",
    "    train_dataset = DataLoader(ArrayDataset(x_train, y_train), shuffle=True, batch_size=train_batch_size)\n",
    "    val_dataset = DataLoader(ArrayDataset(x_val, y_val), batch_size=test_batch_size)\n",
    "    \n",
    "    print('Number of descriptors picked: ',len(x_train.columns))\n",
    "    \n",
    "    # prepare generator here\n",
    "    generator = ParameterGenerator(\n",
    "        in_features=x_train.shape[1],\n",
    "        out_features=1,\n",
    "        h_neurons=dict(\n",
    "            data=lambda x: neuron_vector(x, x_train.shape[1]), \n",
    "            repeat=(3, 4, 5)\n",
    "        ),\n",
    "        h_activation_funcs=(LeakyReLU(),)\n",
    "    )\n",
    "    \n",
    "    # get one random model from the generator\n",
    "    paras, model = next(generator(num=1,factory=SequentialLinear))\n",
    "    \n",
    "    model_name = make_name(model)\n",
    "    persist = Persist(\n",
    "        f'{save_folder}/{model_name}', \n",
    "        # -^- required -^-\n",
    "        \n",
    "        # -v- optional -v-\n",
    "        increment=True, \n",
    "        sync_training_step=True,\n",
    "        model_class=SequentialLinear,\n",
    "        model_params=paras,\n",
    "        author='Someone',\n",
    "        email='someone@email.com',\n",
    "        dataset='Pubchem_ikebata',\n",
    "    )\n",
    "    _ = trainer.extend(persist)\n",
    "    trainer.reset(to=model)\n",
    "    \n",
    "    trainer.fit(training_dataset=train_dataset, validation_dataset=val_dataset, epochs=max_epochs)\n",
    "    # make sure we record the column names of the descriptors that we have picked!\n",
    "    persist(splitter=sp, data_indices=prop.index.tolist(), x_colnames=x_train.columns)  # <-- calling of this method only after the model training\n",
    "    \n",
    "    training_info = trainer.training_info\n",
    "    \n",
    "    summary.append(OrderedDict(\n",
    "        id=model_name,\n",
    "        mae=training_info['val_mae'].min(),\n",
    "        mse=training_info['val_mse'].min(),\n",
    "        r2=training_info['val_r2'].max(),\n",
    "        corr=training_info['val_pearsonr'].max(),\n",
    "        spearman_corr=training_info['val_spearmanr'].max(),\n",
    "    ))\n",
    "\n",
    "# record the summary of the results\n",
    "summary = pd.DataFrame(summary)\n",
    "summary.to_pickle(f'{save_folder}/summary.pd.xz')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot training results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare target and plotting folders, and load models\n",
    "import os\n",
    "from xenonpy.model.training import Checker\n",
    "\n",
    "target_folder = 'Test_HLgap_RandFP500to1k'\n",
    "save_plots = 'Plots_' + target_folder\n",
    "\n",
    "if not os.path.exists(save_plots):\n",
    "    os.makedirs(save_plots)\n",
    "\n",
    "checkers = []\n",
    "model_list = []\n",
    "tmp_file = os.listdir(target_folder)\n",
    "for tmp in tmp_file:\n",
    "    if os.path.isdir(f'{target_folder}/{tmp}'):\n",
    "        model_list.append(tmp)\n",
    "        checkers.append(Checker(f'{target_folder}/{tmp}'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction vs. observation plots (single test trial)\n",
    "import seaborn as sb\n",
    "\n",
    "def draw(y_true, y_pred, y_true_fit=None, y_pred_fit=None, *, prop_name, log_scale=False, file_dir=None, file_name=None):\n",
    "\n",
    "    mask = ~np.isnan(y_pred)\n",
    "    y_true = y_true[mask]\n",
    "    y_pred = y_pred[mask]\n",
    "        \n",
    "    data = pd.DataFrame(dict(Observation=y_true, Prediction=y_pred, dataset=['test'] * len(y_true)))\n",
    "    scores = metrics(data['Observation'], data['Prediction'])\n",
    "    \n",
    "    if y_true_fit is not None and y_pred_fit is not None:\n",
    "        mask = ~np.isnan(y_pred_fit)\n",
    "        y_true_fit = y_true_fit[mask]\n",
    "        y_pred_fit = y_pred_fit[mask]\n",
    "        train_ = pd.DataFrame(dict(Observation=y_true_fit, Prediction=y_pred_fit, dataset=['train'] * len(y_true_fit)))\n",
    "        data = pd.concat([train_, data])\n",
    "\n",
    "    if log_scale:\n",
    "        data = data.apply(lambda c: np.log(c.values) if c.dtype.type is not np.object_ else c, axis=0)\n",
    "#         test_ = test_.apply(lambda c: np.log(c.values) if c.dtype.type is not np.object_ else c, axis=0)\n",
    "\n",
    "\n",
    "\n",
    "#     with sb.set(font_scale=2.5):\n",
    "    g = sb.lmplot(x=\"Prediction\", y=\"Observation\", hue=\"dataset\", ci=None,\n",
    "                  data=data, palette=\"Set1\", height=10, legend=False,  markers=[\".\", \"o\"],\n",
    "                  scatter_kws={'s': 25, 'alpha': 0.7}, hue_order=['train', 'test'])\n",
    "    \n",
    "    ax = plt.gca()\n",
    "    tmp = [data[\"Prediction\"].max(), data[\"Prediction\"].min(), data[\"Observation\"].max(), data[\"Observation\"].max()]\n",
    "    min_, max_ = np.min(tmp), np.max(tmp)\n",
    "    margin = (max_- min_) / 15\n",
    "    min_ = min_ - margin\n",
    "    max_ = max_ + margin\n",
    "    ax.set_xlim(min_, max_)\n",
    "    ax.set_ylim(min_, max_)\n",
    "    ax.set_xlabel(ax.get_xlabel(), fontsize='xx-large')\n",
    "    ax.set_ylabel(ax.get_ylabel(), fontsize='xx-large')\n",
    "    ax.tick_params(axis='both', which='major', labelsize='xx-large')\n",
    "    ax.plot((min_, max_), (min_, max_), ':', color='gray')\n",
    "    ax.set_title(prop_name, fontsize='xx-large')\n",
    "    if log_scale:\n",
    "        ax.set_title(prop_name + ' (log scale)', fontsize='xx-large')\n",
    "    ax.text(0.98, 0.03,\n",
    "            'MAE: %.5f\\nRMSE: %.5f\\nPearsonR: %.5f\\nSpearmanR: %.5f' % (scores['mae'], scores['rmse'], scores['pearsonr'], scores['spearmanr']),\n",
    "            transform=ax.transAxes, horizontalalignment='right', fontsize='xx-large')\n",
    "\n",
    "    ax.legend(loc='upper left', markerscale=2, fancybox=True, shadow=True, frameon=True, facecolor='w', fontsize=18)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    if file_dir and file_name:\n",
    "        if log_scale:\n",
    "            plt.savefig(file_dir + '/' + file_name + '_log_scale.png', dpi=300, bbox_inches='tight')\n",
    "        else:\n",
    "            plt.savefig(file_dir + '/' + file_name + '.png', dip=300, bbox_inches='tight')\n",
    "    else:\n",
    "        print('Missing directory and/or file name information!')\n",
    "        \n",
    "# calculating basic statistics for predictions\n",
    "def metrics(y_true, y_pred, ignore_nan=True):\n",
    "    from sklearn.metrics import mean_absolute_error, r2_score, mean_squared_error\n",
    "    from scipy.stats import pearsonr, spearmanr\n",
    "    \n",
    "    if ignore_nan:\n",
    "        mask = ~np.isnan(y_pred)\n",
    "        y_true = y_true[mask]\n",
    "        y_pred = y_pred[mask]\n",
    "    \n",
    "    mae = mean_absolute_error(y_true, y_pred)\n",
    "    rmse = np.sqrt(mean_squared_error(y_true, y_pred))\n",
    "    r2 = r2_score(y_true, y_pred)\n",
    "    pr, p_val = pearsonr(y_true, y_pred)\n",
    "    sr, _ = spearmanr(y_true, y_pred)\n",
    "    return dict(\n",
    "        mae=mae,\n",
    "        rmse=rmse,\n",
    "        r2=r2,\n",
    "        pearsonr=pr,\n",
    "        spearmanr=sr,\n",
    "        p_value=p_val\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x750 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HjyYUCnH22WezePFiLrvsMq6//nrq6up48MEHOemkk3j77bcpLS0F4M477+TnP/855557LldffTVut5uNGzfywgsv0Nraitfr5dFHH+Wmm26ioKCAn/70p501FBYWJvRrFukrtJawiDjtC1/4AkOHDk267aMPhbHWOl1DQpSWltqysrIDHrNkyRKmTJkSs3NWN7Ry21PLcLsM6Sn7vhZpbgsTiVru/uYxFGSlxuy83TFv3jyuvPJKFi1axLRp0zpvv/fee7n55pt55ZVXOOusszpvr6+v58gjj2TUqFG8+eabABx77LEEg8HOrST3Z8SIEYwYMaLzcbGwZMkS3nvvPSZMmMBpp50Ws+cVERGRfbW0tPD6668zffp0UlMTm1nirMs16TRiEUdvrtpGKBztMhwDpKd4CIWjvLlqe4Ir27/58+dTUlLClClTqK6u7vxoa2tj+vTpvPvuu7S0tACQk5PDli1bePfddx2uWkREROJp69atLF++nMrKSqdLSQiNWMTRotWfkek78Lc40+fhzfLtXHjCsARVdWCrV6+mpaXlgCMQ1dXVDB06lLvvvpuvfvWrnHLKKQwZMoRp06bx5S9/mQsvvJCUlJQEVi0iIiLxYK3FGMPo0aO54YYbyMjIcLqkhFBAjqPGYIgBmQd+G8LrdlHT1Jagig7OWstRRx3FPffcs99jOsLziSeeyPr161mwYAGLFi1i0aJFPP7449x55528++675OfnJ6psERERibHGxkaeeOIJpk+fzogRI/pNOAYF5LjK9HkJRaKkeNz7PSYUiR60yxwP+9sGcuzYsVRVVXH66afjch18AiczM5MLLriACy64AID777+f6667jrlz5/KjH/3ogOcSERGR3qvj53d/uV5td5pBjqPTxg+kMRg+4DGNwTDTSgYlqKLPZWZmAlBTU7PH7Zdffjnbtm3bbwd5+/bP56W7Wt7l2GOP3ed5MzMz9zmPiIiI9E7Nzc1Ya8nIyOB73/seI0eOdLqkhFMHOY6mTRjMwpXbaG4L73cVC6/HxbQJiQ/Ixx13HC6Xi7vuuova2loyMjIYOXIkN9xwA6+++io/+tGPeOONNzj99NPJzs5m8+bNvP766/h8PhYtWgTA+PHjmTp1KieccAJDhgzpXDouJSWFb33rW53nmjp1KnPnzuVnP/sZ48ePx+Vyce655/art2pERESSQXNzMw8++CATJ07kzDPP7LfvAisgx1FBVio3f6mEe/5ZTk1jK5k+D163i1AkSmOwPRzf/KWShC/xBjBs2DAeeughfvOb33DNNdcQCoW44oormDdvHi+99BL3338/jz76KL/4xS8AGDJkCMcffzxXXHFF53PccsstvPzyy8yePZu6ujoGDhzI1KlT+clPfsLRRx/dedxdd91FTU0Nf/jDH9i5cyfWWjZu3KiALCIi0sukpaVx9NFHM27cOKdLcZTWQd5NrNdB7lDd0Mqbq7bzZvl2GoNhMn0eppUMYtqEQY6E475A6yCLiIjETnV1NSkpKWRnZztdSqJ12SJXBzkBCrJSufCEYb1mKTcRERGRDtFolCeffJKMjAxmzJjRb8cqdufoRXrGmExjzB3GmH8YYyqNMdYYM+8Axw8xxswxxgSMMa27fn3GGNPvXu6IiIiIxILL5eL888/n3HPPVTjexekOcgHwC6ASKAO+sr8DjTElwFtAA/AnYAswEDgZSAfq412siIiISF+xdetWampqOPLIIxk6dKjT5fQqTgfkSsBvrd1ijPEAoa4OMu0vZ+YDAeBUa21jAmsUERER6XPeeecdtm/fTklJCR6P05Gwd3H0u2GtbaW9E3wwpwNTgHOttY3GmDQgbK3tMlCLiIiIyIF97WtfIxgMKhx3IVk2Cjlr169Nxpj3gGYgaIx5wxgz0cG6RERERJJGRUUFzzzzDJFIpL+uWtEtyRKQOxbje4r2MYtvADcDk4C3jTHFsTpRf1n2Lpnpz0hERKRnduzYwfbt2wkGg06X0qslS089c9evH1lrL+y40RhTBrwL3EJ7YN6DMeb7wPehfWOMg/F4PLS1tZGaqrWJe7NQKKSQLCIicghCoRBer5cpU6Zw9NFHa6ziIJKlg9yy69fHdr/RWvsvoAI4tasHWWvnWGtLrbWlhYWFBz1JTk4OO3bsOMxSJd5qa2tpbW3FWovb7Xa6HBERkV5t7dq13HfffVRVVQEoHHdDsgTkrbt+3dbFfduBvFicZPDgwVRVVbF169bOACa9h7WWhoYGtmzZwrZt2wiHw3TnhY+IiEh/NmDAAIqLi8nKynK6lKSRLC8hPqR9VMLfxX1+urcSxkH5fD6OOOIIlixZwpYtW3C5kuX1Q/8RiUTYvn07O3bsIDU1leHDhztdkoiISK/02WefMXDgQPLz87noooucLiepJEtAfh6YDXzXGDPPWhsBMMacAxQDD8XqRD6fj9LSUhYsWMDGjRu1o0wvZK0lOzub8847j/T0dKfLERER6XU2b97MvHnz+OpXv8qkSZOcLifpOB6QjTHXA7l8Pu4xyRhz+67fv2CtXW6trTLG/Az4H+ANY8zTwBDgBmAjcG8sa0pNTeW8886jsbGR+vp6otFoLJ9eDoMxBp/PR35+vl68iIiI7Iff7+eMM86gpKTE6VKSknF6ztYYUwHs733yK62183Y7dgZwE3AE7VtOvwT8xFpbebDzlJaW2rKyssMtV0RERKTXWrlyJWPGjMHn8zldSrLostvmeAfZWjviEI6dB8yLVy0iIiIiyaquro6///3vTJ06lTPPPNPpcpKa4wFZRERERA5fTk4OM2bMoKioyOlSkp6WaRARERFJYu+++y7r168H2mePtUfA4VNAFhEREUlSoVCIlStXsmrVKqdL6VM0YiEiIiKSZDoWWfB6vcyYMYOUlBSHK+pb1EEWERERSSLWWhYuXMg//vEPrLX4fD5tbhZj6iCLiIiIJBFjDF6vF6eX6u3LFJBFREREkoC1lqamJjIzMznttNMAtGlWnKgfLyIiIpIEFixYwNy5cwkGgxhjFI7jSB1kERERkSQwadIkMjMztUteAiggi4iIiPRSkUiETZs2MWrUKIYMGcKQIUOcLqlf0IiFiIiISC+1ePFiHn30UaqqqpwupV9RB1lERESkl5o6dSoFBQUUFhY6XUq/og6yiIiISC8SCoVYtGgR4XAYr9fL+PHjnS6p31FAFhEREelFKioqeOedd6ioqHC6lH7L9JdFpktLS21ZWZnTZYiIiIgcVE1NDfn5+U6X0R90uVaeOsgiIiIiDgsGgzz22GNUVlYCKBw7TAFZRERExGGtra3U1NRQX1/vdCmCRixEREREHNPW1obX68UYQyQSwe12O11Sf6MRCxEREZHeIhgMMnfuXN5++20AheNeRAFZRERExAGpqakMHz6coUOHOl2K7EUbhYiIiIgkUH19PR6Ph/T0dM455xyny5EuqIMsIiIikiDRaJT58+fz1FNP0V+uA0tG6iCLiIiIJIjL5eKss87C5/NhTJfXh0kvoIAsIiIiEmc7duygpqaGsWPHMnr0aKfLkYPQiIWIiIhInL366qu8+OKLhMNhp0uRblAHWURERCTOzj//fJqamvB4FL2SgTrIIiIiInFQWVnJyy+/TDQaJS0tjYKCAqdLkm5SQBYRERGJg4qKCtasWUNTU5PTpcghUp9fREREJIai0Sgul4sTTzyRyZMn4/P5nC5JDpE6yCIiIiIxsmnTJh544AFqamoAFI6TlAKyiIiISIz4fD7S09Pxer1OlyKHwfSXXVxKS0ttWVmZ02WIiIhIH1RfX092djYA1lptApI8uvyDUgdZRERE5DAEAgFmz57Nxx9/DKBw3AcoIIuIiIgchsGDB3PCCScwatQop0uRGFFAFhEREemB9evX09bWhsfjYfr06aSlpTldksSIArKIiIjIIaqrq+OJJ57grbfecroUiQOtgywiIiJyiHJycvjWt77FsGHDnC5F4kAdZBEREZFuWrp0KYFAAIAxY8aQkpLicEUSDwrIIiIiIt0QCoX417/+xQcffOB0KRJnGrEQEREROQhrLV6vlxkzZpCenu50ORJn6iCLiIiIHMA777zDq6++irWWrKws3G630yVJnKmDLCIiIrIf1lrq6+tpbW3VDnn9iAKyiIiIyF6stbS1tZGamso555yDtRaXS2+89xf6kxYROQzRqGXVljpeXVHJqi11RKPW6ZJEJAYWLVrE3LlzCQaDGGMUjvsZdZBFRHooGrXc/9oaytZXE2ltw52aQunoAq49cxwul96GFUlmo0aNIhwOk5qa6nQp4gAFZBGRHiqvrKdsfTVZFWshGASfjzKgfOJgJhTnOF2eiBwiay2VlZUMGTKEESNGMGLECKdLEofo/QIRkR7aUtNMpLUNgkFMSgoEg0Ra29hS2+x0aSLSA4sXL2bu3Ll89tlnTpciDlMHWUSkh4rz03GnpoDPh93VQXanplCcpzVSRZJRaWkpPp+PwsJCp0sRhykgi4j0UElRNqWjCyiDPWaQS4qynS5NRLopEolQVlbGcccdR2pqKlOmTHG6JOkFFJBFRHrI5TJce+Y4yicOZkttM8V56ZQUZesCPZEksm7dOl555RXy8vIYN26c0+VIL2Gs7R9LEpWWltqysjKnyxAREZFeZuvWrQwZMsTpMsQZXXY0dJGeiIiI9CuhUIjnnnuO6upqAIVj2YcCsoiIiPQrDQ0NbNiwga1btzpdivRSGrEQERGRfiEajXbuiNfa2qpNQAQ0YiEiIiL9VWtrK/PmzeP9998HUDiWA1JAFhERkT7P4/GQnZ1NVlaW06VIEtAybyIiItJnNTc343a7SU1N5cILL3S6HEkS6iCLiIhInxSNRpk/fz5PPfUU/eWaK4kNdZBFRESkT3K5XJx88sn4fD6M0QY+0n0KyCIiItKn1NfXU1dXx9ChQ5kwYYLT5UgS0oiFiIiI9CkvvfQSTz/9NOFw2OlSJEk52kE2xmQCtwJTgFJgMPCItXbGQR53OvD6rk/HWmvXxbNOERERSR5f+cpXaGhowOPRG+XSM053kAuAX9AekLu1i4cxJgX4A9AUx7pEREQkidTU1PDmm29irSUrK0vbR8thcTogVwJ+a+0Q4GvdfMwtQD7wYNyqEhERkaSycuVKPvzwQxoaGpwuRfoAR997sNa2Alu6e7wxZjhwO3A9MDxedYmIiEhysNZijOGUU05h8uTJ2ghEYsLpDvKhmg0sB+Y5XIeIiIg4bNu2bTz88MM0NDRgjFE4lphJmul1Y8xXgK8Ax1trbXfWMzTGfB/4PsCwYcPiW6CIiIgkVCgUIhgMEgqFnC5F+pik6CAbY9Jo7x7/2Vq7pLuPs9bOsdaWWmtLCwsL41egiIiIJEwwGARg6NChXH311eTn5ztckfQ1SRGQgZ8Cubt+FRERkX6qsrKS3/3ud5SXlwPtu+WJxFqvH7Ewxgyhfa3kWUCuMSZ3110dLxeHGWMi1tqNjhQoIiIiCTNgwABKSkq0jJvEVa8PyMBAIBX4z10fe3sdqKO9wywiIiJ90NatWxk0aBApKSmcf/75TpcjfVwyBOSNwDe6uP2bu27/IbA5oRWJiOwSjVrKK+vZUtNMcX46JUXZuFwHv4hYRLqvvr6ehx9+mOOPP57p06c7XY70A44HZGPM9bR3fzuGiCYZY27f9fsXrLXLgb918bgjd/32FW01LSJOiEYt97+2hrL11URa23CnplA6uoBrzxynkCwSQ9nZ2Zx33nmMHj3a6VKkn3A8INM+X7z7ph+Td30ABGhf91hEpNcpr6ynbH01WRVrIRgEn48yoHziYCYU58TlnDYYJBwI4PH7MT5fXM4h0lusXr2a/Px8Bg0axFFHHeV0OdKPOH7pp7V2hLXW7Odj3gEed8euY9Q9FhFHbKlpJtLaBsEgJiUFgkEirW1sqW2Oy/lsMEjNzBupnXkjNTNvxO5a6kqkLwqHwyxYsIA333zT6VKkH+oNHWQRkaRUnJ+OOzUFfL72sOrz4U5NoTgvPS7nCwcCRAIB3IWFRAIBwoEA3jFj4nIuEad5PB6uuOIKMjIynC5F+iEFZBGRHiopyqZ0dAFlsMcMcklRdlzO5/H7cfv97SHZ7982YvYAACAASURBVMfj98flPCJOWrp0Kc3NzZx88snk5eU5XY70UwrIIiI95HIZrj1zHOUTB7OltpnivPiuYmF8PvJnz9IMsvRZ1loqKipoaWnhC1/4gjYBEccYa63TNSREaWmpLSsrc7oMERER6UIkEsHtdhONRolGo3g86uFJQnTZ0dBLMxEREXHU4sWLeeihh2htbcXlcikci+MUkEVERMRRAwYMoLCwEK/X63QpIoBmkEVERMQB1lpqa2vJz8/niCOO4IgjjnC6JJFO6iCLiIhIwn3wwQc88MADVFVVOV2KyD7UQRYREZGEO/LII2ltbaWgoMDpUkT2oQ6yiIiIJIS1lhUrVmCtJSMjgy9+8YsYE59lEUUOhwKyiIiIJMSaNWt49tlnKS8vd7oUkQPSiIWIiIgkxLhx47j00ksZNWqU06WIHJA6yCIiIhI30WiUhQsXUldXhzGG0aNHa6xCej0FZBEREYmb2tpali5dytq1a50uRaTbtNW0iIiIxJy1trNT3NjYSGZmpsMViXRJW02LiIhI/IXDYZ588kmWLVsGoHAsSUcBWURERGIuEokQiUScLkOkR7SKhYiIiMREW1sbLpcLj8fDJZdcoovxJGmpgywiIiKHLRqN8vjjj/P000/vMX8skozUQRYREZHD5nK5OOqoo/D5fArHkvQUkEVERKTHWlpaqK+vZ9CgQUyZMsXpckRiQiMWIiIi0mPPP/88jz32GKFQyOlSRGJGHWQRERHpsbPOOova2lq8Xq/TpYjEjDrIIiIickgaGhr44IMPAMjLy2PUqFEOVyQSWwrIIiJ9hA0GCa1bhw0GnS5F+rgPP/yQ119/nbq6OqdLEYkLjViIiPQBNhikZuaNRAIB3H4/+bNnYXw+p8uSPmratGlMmjSJnJwcp0sRiQt1kEVE+oBwINAejgsLiQQChAMBp0uSPqampoYnnniClpYWXC4XBQUFTpckEjcKyCIifYDH78ft9xOpqsLt9+Px+50uSfqYnTt3UllZSX19vdOliMSdsdY6XUNClJaW2rKyMqfLEBGJGxsMEg4E8Pj9Gq+QmAmHw3g8nn1+L9JHdLmrjTrIIiJ9hPH58I4Zo3AsMVNVVcV9993H+vXrARSOpd9QQBYREZEuZWZmMnjwYHJzc50uRSSh9FJQRERE9rBjxw7y8vJIS0vj4osvdrockYRTB1lEREQ61dfXM2fOHBYtWuR0KSKOUQdZREREOmVnZ3PGGWdQUlLidCkijlEHWURERKioqKC2thaA448/nuzsbIcrEnGOArKIiEg/Fw6HefbZZ3nllVecLkWkV9CIhYiISD/n8Xi45JJLyMrKcroUkV5BAVlERJJGNGopr6xnS00zxfnplBRl43J1uc6/dMPq1atpbm5mypQpDBo0yOlyRHoNBWQREUkK0ajl/tfWsGRjDRFrcRvDlJH5XHvmOIXkHrDWsnz5cpqampg8eTIul6YuRTooIIuISFIor6xnycYa8jJSMMZgraVsQw3llfVMKM5xurykYq3FGMMFF1xAJBJROBbZi/5FiIhIUthS00xkV7ADMMYQxbKlttnhypLLRx99xPz58wmFQng8HlJTU50uSaTXUUAWEZGkUJyfjntX5xjau6AuDMV56Q5XllxcLpc6xiIHoRELERFJCiVF2UwZmU/ZhhqitIfj0lH5lBRpvd7uaGpqIiMjg6OPPppJkyZ1duJFZF8KyCIiElc2GCQcCODx+zE+X4+fx+UyXHvmuPZVLGqbKc7TKhbdtXTpUhYuXMh3v/tdCgsLFY5FDkIBWURE4sYGg9TMvJFIIIDb7yd/9qzDDskTinN0Ud4hGj16NMcccwz5+flOlyKSFDSEJCIicRMOBNrDcWEhkUCAcCDgdEn9hrWW9evXY60lJyeHs88+G7fb7XRZIklBAVlEROLG4/fj9vuJVFXh9vvx+P1Ol9RvrFmzhvnz51NeXu50KSJJx3RcDdzXlZaW2rKyMqfLEBHpd2I1gyyHxlrLihUrOOqoozRzLLJ/Xf7jUAdZRETiyvh8eMeMUThOAGst7777Lk1NTRhjtFqFSA8pIIuIiPQRO3bs4K233mL58uVOlyKS1LSKhYiISB9RUFDA1VdfrdUqRA6TOsgiIiJJLBqN8vzzz3dejDdgwACNVYgcJnWQRUREklg4HKaqqooBAwY4XYpIn6GALCIikoQikQjGGFJSUpgxYwYej36ki8SKRixERESSjLWWp556iueeew5rrcKxSIzpX5SIiEiSMcYwbNgwUlJSNG8sEgcKyCIiIkmira2NpqYm8vLyOOmkk5wuR6TP0oiFiIhIknj++eeZN28eoVDI6VJE+jR1kEVERJLEqaeeSlVVFV6v1+lSRPo0dZBFRER6sZaWFlasWAHAwIEDmThxosMVifR9CsgiIofJBoOE1q3DBoP96tySGIsXL+b5559n586dTpci0m84OmJhjMkEbgWmAKXAYOARa+2MvY6bAlwGnA6MBJqAj4FfW2tfS2TNIiK7s8EgNTNvJBII4Pb7yZ89C+Pz9flzS+JMmzaNkpIScnNznS5FpN9wuoNcAPyC9oBcdoDjfgRcAiwGbgF+CwwEXjXGXBPvIkVE9iccCLQH1MJCIoEA4UCgX5xb4quhoYHnn3+etrY23G43xcXFTpck0q84HZArAb+1dgjwtQMcNxsYaq292lo7x1p7D3A8sAa4yxijiw1FxBEevx+330+kqgq334/H73f83Bq7SH5bt25l9erVVFdXO12KSL9krLVO1wDArpAboosRiwM85n+Bm2kPzwdsnZSWltqysgM1qUVEesYGg4QDATx+f8JHHPY+t8Yukls0GsXlau9dtbS0kJaW5nBFIn1elzvtON1BPlxDgDBQ63QhItJ/GZ8P75gxjgTRvc+tsYvkVVtbyx//+Ec2b94MoHAs4qCkDcjGmAnA14EXrLVN+znm+8aYMmNMWVVVVWILFBFxgJMjH3J4PB4PqampWuNYpBdIyhELY0w28G/aO8hHW2s3H+z5NWIhInuL1WiEkyMWyVDPoUr2+g9VQ0MDmZmZGGOw1mJMl+/4ikh8dPkPLukubjPGpAH/AEYBZ3cnHIuI7C1Ws7q9cea3Y+wiGSXy+9kbgnhDQwN/+tOfOO644zj11FMVjkV6iaQasTDGpADPAScC37DWvuVwSSKSpGI1qxsOBAhv3oxJTye8ebNmfg9TomaoO4J47cwbqZl5o2MrfmRmZlJaWtq5O561lscXV7C1ttmRekSkXdIE5F0jGE8B04HLrbUvOlySiCSxWM3qugsLiVZV0bZsGdGqKtyFhTGuNL6iUcuqLXW8uqKSVVvqiEadHbtL1Ay10xczVlZW0tDQgDGGadOmUVBQgLWW37+6htkLPuH6R8rY0dCa0JpE5HNJMWJhjHEBfwHOB75vrX3S4ZJEJMkZn4/82bMO+y32SFUV7oGFeEaOxDY2EqmqwpWTE+Nq4yMatdz/2hqWbKwhYi1uY5gyMp9rzxyHy+XMW/2x+nM5mM4gvmuUI5EXM4bDYZ588kkGDhzIJZdcArT/Wdzzz9X87YNPAZhYnENOui7WE3GK4wHZGHM9kMvn3exJxpjbd/3+BWvtcuB/gIuBt4AWY8ylez3Nq9ba7QkpWET6jFjM6nr8ftxDh7UHrWHDkmrViPLKepZsrCEvI6XzArGyDTWUV9Yzodi5kJ+IGepEBfGueDweLrzwQrKzswGIRC13P7+Sl5ZtBeArk4v5yXkTcTv0IkVEekFABm4Fhu/2+eRdHwABYDlw7K7PT931sbfTAAVkEUk4J4PW4dpS00xkt1UTjDFEsWypbXY0ICdKoi9m3LhxI83NzUycOJGhQ4cCEI5E+eWzK3h15TYALjx+KDd/abxjHXwRaed4QLbWjujGMdPiX4mISM8k66oRxfnpuHdbWsxaiwtDcV6606UlFdvSQuuHH+L74hf3f4y1/Otf/6KxsZHx48fjcrloC0e5/en/4+3yzwC45KQRXD99nFayEOkFHA/IIiLijJKibKaMzKdsQw1R2sNx6ah8SoqynS4tadhgkB3f+S6t7/6LvHvvIf3CC7o8zhjDN77xDcLhMC6Xi2BbhP988iPeX78DgO9NG813p41WOBbpJRSQRUT6KZfLcO2Z4yivrGdLbTPFeemUFGXr7f1usq2t7PjeVbS+/Q4A0YaGfY4pLy9n5cqVfO1rXyM1NZXU1FSaWsPc+thSPtpUC8B108dx2ckjE1q7iByYArKISD/mchkmFOcc9sxxb9h0I5Fsays7rvoBrYveBCDnjl+QeeWMfY6rr69n586dhMNh3G439S0hbpq/hI8DdQDces54LjxhWAIrF5HuUEAWEZHD0ht3E4wn29ZGzTXX0vr66wBk/+x2Mq/63h7HtLW1kZKSwvHHH8+UKVNwu93UNrVxw1/KWLOtAWPgtvMmcu6xybPqiUh/kjQbhYiISO/k9KYbiWRDIWquu57ggoUAZN/2E7Ku/sEex6xcuZL77ruPmpoaANxuN9UNrVz78Aes2daA22X45QWTFI5FejF1kEVE5LA4uelGItlwmJrrfkjw5X8CkP3jH5F13bX7HDd48GBGjhxJVlYWANt2tnD9I2UEaprxug13fuNoTh0/KKG1i8ihMdY6u61oopSWltqysjKnyxARianeMvvbW+qIFxsOUzvzBlqefwGArFtuJvvmm/Y4prKykqKioj1u+3RHEz98pIxtdUFSPS5+c/Fkpo4pSFjdInJQXV6VrBELEZEk1TH7WzvzRmpm3ogNBh2rpWMt6D4ZjiMRam+6+fNwfMNMsm66cY9j1q5dy5w5c1i9enXnbRurGrnm4Q/ZVhckLcXNPZdOUTgWSRIKyCIiSao/zf46xUYi1N58Ky3PPgdA5vXXkfWjW/dZr3j06NGcddZZjBs3DoC12+q59uEPqW5oJdPn4b7LS5kyMj/h9YtIzyggi4gkqc7Z36qqPj376xQbjbLzx/9Jy9/+BkDmD75P9n/95x7h+KOPPqK1tRWXy8XUqVNxu918HNjJtQ9/SG1TGznpXn5/xXEcOTTXqS9DRHpAF+mJiCQp4/OR99+/obVsCamlUzSDHEM2GmXnf/2E5if/CkDG975L9s9u3yMcV1dX8+KLL9LY2Mgpp5wCwLJNtdz82BKaWyMMyEzhviuOY9TATEe+BhHpOQVkEZEkZYNBan/0n0QCAZodXH+4r62DbK2l7qe30/zY4wBkXDmDnDt+sc9YRUFBAd/5znc6L8x7f301P37iI1pDUQbl+LjvilKGDchIeP0icvg0YiEikqR6ywxyLOuwwSChdescu+DQWkvdz39B018eBSDj8svI+dX/6wzH1lpee+01Nm7cCEBxcTEul4t3PvmMWx9bSmsoSnFeGn/8zvEKxyJJTB1kEZEkdSjrD0ejlvLKerbUNFOcn05JUTYuV5erG8W1jgNxuhNtraXujl/S9NDDAKRf8m1y7rpzj85xW1sba9asAWDkyJEAvLZyG794ZjmRqGV4QQb3XVHKwOzk7aCLiNZBFhFJat2Z/Y1GLfe/toYlG2uIWIvbGKaMzOfaM8fFLCTHYgY5tG4dtTNvbO9EV1WRN3sW3jFjYlLfwVhrqb/zLhr/+CcA0r91Ebn//VuMy9V5P4AxhtbWVlJSUjDG8NKyLdz195VELYwdnMXvLptCfmZqQmoWkZjo8j9BdZBFRJJYx/rDB1JeWc+SjTXkZbSHOmstZRtqKK+sZ0JxTsLqOBinduSz1lL///3m83D8jQv3CcfPP/88Xq+Xc845h9TU9gD87Ief8tsXVwEwoTibey+dQk56SkJqFpH4UkAWEenjttQ0E7G2c1TAGEMUy5ba5pgF5A6H00k2Ph/5s2clfDWMhv/5Xxp//wcA0r7+NXL/9386w3GHjIyMzq4xwOOLK5i94BMAjhmex/9++1gyfPqRKtJX6F+ziEgvFovZ4eL8dNy7OscdHWQXhuK89JjWGosZ4lh0og9F/b2zaJj1OwDSzj+PvHvvwbjdAEQiEZqbm8nKyuLMM8/s/N49/PYG5ryxDoDjRg3gtxcfQ1qKfpyK9CX6Fy0i0kvFana4pCibKSPzKdtQQ5T2cFw6Kp+SouyY1tvVahaJDLuHquF3s2n4n/8FIO0rXyFv9u8wns9/LP7jH/9g8+bNXHPNNXi9Xqy1PPDaWv7ybvsKFicfUchd3ziaVK/bkfpFJH4UkEVEeqlYzQ67XIZrzxzX3omubaY4L7arWHRwaoa4Jxr+cD/1v/1vAHxfOpu838/eIxwDlJaW4vf7O8Pxvf8s56n3NwNwxsRB/PKCSXjcWi1VpC9SQBYRccjBxidiOTvschkmFOfEfOZ4d07NEB+qhj/+ifq7fw2A7z+mk3//HzBeLwChUIhNmzYxZswY/H4/fr+fSNTym398zAtLtwBwztFDuO38iQrHIn2YArKIiAN2H58IR6OEwhZ/fhozvjiaCcU5uFwmYbPDsZToGeJD1fjgn6n/1Z0ApJ5xBvl/fACT8vnKE2+99Rb//ve/+eEPf0hubi7hSJT/99xKFq6oBOBrpUP50ZfHx7z7LiK9iwKyiIgDOsYnctO9bN7RzM7mNip3thCoaeHkIwq59sxxCZsd7i8aH55H3R2/BCD19NMY8OCfMKl7rll86qmnMmLECHJzcwmFo/zsb//Hm6s/A+DiE4cz86wj9tlyWkT6HgVkEREHdIxPNLVF2NnchtftAqKkeMwec8aJmB3uDxof+Qt1t/8MgNQvnsKAB+d0huNgMMg777zDaaedhtfrZcyYMQRDEX7y12X8e201AN85dRRXnTZG4Vikn9AAlYiIAzrGJ4JtEbDQnrsMaSmezjlj+Hx2ePqRRZ2jF7uzwSChdeuwwWDiv4gk0fTY49Td9lMAUk86iQEPzd1jPnrDhg28//77bN26FYDm1jC3PLa0Mxxfc8ZYvn/6WIVjkX5EHWQREQd0jE+8U15FOGqxoSh5GSlkpLppDUW7NWcci3WH+7qmJ59k54//E4CUE6eS/8jDmLQ0gM7Z7gkTJlBcXExOTg6NwRA3zV/Kik93AnDT2SVcdOJwx+oXEWeogywichiiUcuqLXW8uqKSVVvqiEZttx7XsfTaz75+JMeNzmdgto+MVA81jaFuzxmHAwEimzaBMUQ2VRAOBA73y+lTmp/+Gztv/TEAKSccz4BH5uHaFY4bGxt55JFH2L59OwA5OTnUNbdx/SNlrPh0J8bAf507QeFYpJ9SB1lEYiIWO74lm46VKMrWVxNpbcOdmkLp6IJub+ThchmO9Ofy228d26M5Y1d2NqG1a6GtDVJScGXr4r0Ozc8+R+1NN4O1pJSWMuAvj+DKyOi8PxQK0djYSHNz+yjLjoZWZv6ljPWfNeJ2GW7/6pF86eghTpUvIg5TQBaRwxarHd+STXllPWXrq8mqWAvBIPh8lAHlEwcf8kYePVmjuG3FSjAGk5ODDQZpW7GStDNO7/LY/vQCpvn5F6i94UawFu/kyQyY/xdcmZlA+wV5Pp+PvLw8rr32WlwuF5/VBbn+kQ/ZvKMZj9vw/y6cxOkTBjv8VYiIkxSQReSwxWrHt2SzpaaZSGsbBIOYlBRsMEikta1HG3n0RGrpFFy5uUTr6nDl5pJaOqXL4/rTC5iWF1+i9oczIRrFe/QkCh6fjysrC2gfq5g7dy6lpaWcdNJJuFwuttY2c928Mip3tpDicfHri47hpHGFDn8VIuI0BWQROWyx3PEtmRTnp+NOTQGfr30VCZ8Pd2pKwjbycOXkMOiN12gtW9IelnO6/l73lxcwLf/8JzXXXQ+RCN6jjqLg8cf2GDtJT09n7NixjBw5EoBN1U1c/8iHVNW34vO6+e9vT+a4UQOcKl9EehEFZBE5bMm441sslBRlUzq6gDLYYwY5kRt5mNRUPMOH7bPhxe4S+QLGqVGOloULqbn6WgiH8U6cSMETj+HKzQVgx44dpKenk5aWxjnnnAPAuu0N/PCRMmqb2shI9XDPpcdy9LC8uNcpIslBAVlEDlt/3fHN5TJcfcZYFhRmUl5ZT0lRNmdNKkrY2EJ3l3lL1AsYp0Y5gq+/Qc33r4ZwGM/4EgY8+TiuvPawGw6HefTRRxk4cCDf/va3ASjfWsfMvyyhviVEdpqX2ZdPoWRI3+mki8jhU0AWkcPWsWRZf9vxLRq1/PH1tZ2BcOWnO9lY1Ziw2d5wINAejgsLiQQChAMBvGPG7HNcol7A7D7KAdAQDPHGx9sZWZjJl44eEpfvSfDNN9nxvasgFMJzxDgK/vok7vz8zvs9Hg9f/vKXyd3VTf6/zbXcPH8pTa1h8jJSuO+KUsYMyop5XSKS3BSQRSQmeroSQzJzerbX4/fjKioivHEj7pEj8fj9XR6XqBcwHaMc0D7fu7O5jVDEMvet9XF54RB8+x12fOd70NaGZ8yY9nA8oH2GuLKykqamJsaMGcPYsWMBKNuwg1sf/4hgKEJhdiq/v+I4hhdkHOgUItJPaaMQEZEeOtBsb6IYA5iOrar372BbVsdCxyhHQ3MrOxuDeF0Gr9tFQWZK5wuHWGl991/suPJKaG3FM2oUBU89ibvw89UnXn31VV555RWi0SgAi9dUcfNjSwmGIgzJS+NP3zle4VhE9ksBWUSkh3af7QXiNttrg0FC69a1r5Sxm3AgQGRrJR7/UCJbK7u9k97+nm9/t3dXSVE2xw7N4bNNlYSCbbQ1NZOb5iErzRvTFw6t//43O2ZcCcFW3CNGtIfjQYP2OObCCy/ksssuw+VysWjVdn785Ee0haMMG5DOH688niF9/AJSETk8GrEQEemhRMz2HuhCPI/fj9vv77xvfyMW3Xm+7l7wdyAul+H7Y70UPPFv/jp0KnnBenKH5mItMXvh0Pr+++y4fAa2pQX38GEUPv0U7qIiACoqKli1ahVf+tKXSE9vP9cry7fyq+dWEolaRg/KZPblpQzI3P+KHyIioIAsItJjiZjtbfv0U8pr29g+YgqDqj6l9NNPSd01U2t8PvJnzyIcCODx+7sVaMOBAOHNm3FlZhLevLnzwr6DXfDX3eXbUoYO5cy0Rj6tWsPyglHsCBtcjW0xeeHQ+mEZOy67AtvcjHvoUAqefgr3kKLO+zdv3szGjRsJBoOkpaXx97JP+c2Lq7AWSoZk87vLppCTnnJYNYhI/6CALCJyGOJ5cWI0apmzNsQHY6YTCYVxjynh+LUhrhttO8Op8fm6XLmiKzYYxDY0EPnsM8Lr1uHKyemc2z1QN3qP5dsiEUwoROnYQVx39vh9QrLx+SiYPYsbPv2Ude4ctjZHYvLCoW3pR+y49DJsUxPu4mIKnv4rnuJiACKRCG63m1NOOYWpU6eSkpLCX/+9iXtfKQfgqKG53HvpsWT6vD0+v4j0LwrIIiK9VHllPUs/raNw/GgItoIvlSWb6/ZZJcMGg4QDAVzFxXxS09Zll7djhCK8fh20tOCdNAmam4lUVeHKyTlgN7pztY40N+GPPyEaDPL+tu2cXlLAxFED96nb+Hykjh3LRGBiDL4PbcuWUX3JpdjGRtxFRe3heOhQANasWcOCBQu47LLLyM3NJSUlhb+8s4H7X1sLQOnIfH578WTSU/XjTkS6T/9jiIj0Up2rZLg9kNH+33WU8B474HUE31AgwKPjz+LjMccSNWafTTo6RyiGDCFSuQ27cyeeMWP26BTvrxvduXxbsBUbDOJKSSEaCvHphq1dBuSOwN7dsY8DaVuxgupvX4qtr8c1eFB7OB4+vPP+rKws8vLySE1NxVrLnDfW8fDbGwD4wtgC7r7oGHxe92HVICL9jwKyiEgv1Z0d8DqC78biI/g/k0OhK4IrM3OfNZk9fj/uIe1rJqeecAJZt96Cd/SobgXYjjrwpUJqKtHmZlyZaQwdNWSfY2NxsV+HtpUfU/2ti7F1dbgGDqTgqafwjBwJQG1tLXl5eRQVFXHppZdireV3Cz7hyX9vAmDa+IH86sKj8Xq0WJOIHLoe/89hjMkwxgw1xgzb+yOWBYqI9Fcdq2TUNLZR1RCkpouL3Tpmh7e0RLHeFFzpaUDXazJbC1iwLle3w3FHHceOyGPLziCbvNlU+nKZ1PYZJUWf70DXsURcaP2GfS7264nQqtXs+NbF2J11uAoLKXj6r3hHjwJgw4YN/P73v+eTTz4B2mekf/vi6s5wfNakIu78hsKxiPTcIXWQjTFe4HbgB0DhAQ7V+1kiIoepO6tkdMwOj1u2Du9HdVjjwrDvmszhQIBoZSWeoUOJVFbud1vq/TMQCkMkgsvrxVY3EQ4EcI8du2fXeEgRrqIiIpWV3V56bm+h8nKqL/oW0dpaXAMGUPDXJ/aoddiwYf8/e3ceH1dZL37885xzZk0yk22SNJmmTdMlbbrQNrQsRRAQRBZFWfQqLtflKigqAuJyRfGi14uIIOh1AxdEUfzpBS5craCAUOi+0y1Nm0z2ZNKZZGbOzJxznt8fk4SkTdukTUvR5/169dUksz055BW+8+134eyzz6ampgbLdvjG/2zjqU1tAFyxpIrPX16P/g++5lxRlBNroiUW9wEfA54E/gr0TfqJFEVRlGHjmZIhvF7mL6unIb7rsDOZj2Vm8pAd7XHW74tSFcrH6mrGSZlsKZ3BHj1IPYweEdfWTuGdX8eO9uFpWDrh8ors7t30XPsenGgUragoFxzPmZM7x44d1NbW4nK5OP/888laDrf/fjPPbOsE4Jrl1Xz2krrhzYaKoijHaqIB8jXAL6SUHzoRh1EURVGOzdGyzeOZmXy45rqRzYKueXNxDsTQdB9tSZt6Dgq+K6cQv//7OO3tJCdYg5zd00jPNe/G6elBFBZS+uhvcM2dizRNOrdv57dPPsm5557LueeeSzpr88XfbuLFXd0AvH9FDZ+4cJYKjhVFmRQTDZBdwKoTcRBFURTl+Bwt23ykmckHkL3H/QAAIABJREFUN9cV3fUt7O5ujHD4tWZB2yK7bTsymcTJL6bSP2f4eYeCb5nOcOCWWw+7cORwrL1N9FxzDU5XFyIYpPQ3j+Cqnzd8Li0S4fLaGSxoaCCVsbj11xtZs7cXgH87fyYfOrf2GK+aoijKoSYaID8LNAA/OgFnURRFUV4nI8skrOZmotffgNN3AD0cZs6997BkehEvbI6Qst24dY2Gpo3UJhcCuTFvQ8G3NM3haRlGTc24Sjms/fvpueZanM4uRCBA6SMP416wAIAXV66ksK+PKaEQVY17GdjfyudX9bGp+QAAn754Du85a/qJuiyKovyTmmiL76eA84QQnxJCqGX2iqIo/yCMcBhtyhSslhZEYSFONIoIhdjRl+FPf99BTyyF3dsDjgTpDD5Kjvlc0raR6TTSto/6ulZLCz1XX4vd3o7Iz6f0Vw/jPu00ANLpNOsaG9k1fRp2dzfJqTV89rme4eD41svmqeBYUZQTYqIZ5BcBH/Bd4DtCiC7g4N+AUko57ZBHKoqi/INzHJmrAR5jk92JMpGlHNI0yQyugG49YFKRiVO/qBbd58sFtIkESInu82Hn5fGQVsOWyhrS2/vpjKUocgTVyR6kZbFt2kIa/WWHbMrLNjaSeWU1SEkmuppsYyPu+rH36VmRSC44bm1F5OVR8vAvcS9ZjJS5wNvj8fDhj3wEn6bR29jMl5/vYU9HP5qAL71jPpeeVjUZl1BRFOUQEw2Q93K4lIGiKMo/qaHA80e7s6xviWFLecgmuxP1uuNdyiFNk54bP8NDspotpTXYErRMmkWPPsNnb/8Afdd/kszatWgFBdjt7bR/6U62reuj1KPRK3V0TSPu9lPkzSNfOGhTpw436Y0mQEqkbSN0Pfc5hwbyVmsbPddci93SgvB5KX7wJ3hOb0BKycqVK9F1nfPPP5/8/Hy64iaf+lsP+3sS6JrgjqsWckF9xQm5poqiKDDBAFlKed4JOoeiKMob0lCQuqMvw+qZbyE0txahG4dssjsRRo1XO0pDnBWJsKsvw5ba6QT6owjTRCLY6AuweeXLVEWjaPn5OLEYxpzZdLgDWJ07sRJ9GN48RHAKUoDpCPxmAqe1lUp/3SGvY4SrwOeDAwcgPx8jXHVIIB/84m30vPc67P3NoOtooRCJn/0cT0MDeDyk02l0PTdOv60vxad+vobWvhQuXfCNa0/jnDmHrrdWFEWZTGrVtKIoynEYClI7py/FzlpgpiHPGLXJ7kQFyOOZbTxU9hGJu9kdno9l2WgeDzKdRgCOEHR6g0wLV2Hu3w8uF3h9lCdyQbSTTuNLJMnXvPTpPtK6mwOefBZ27mKm3cBQk95QhlimM+hlZWgzZmDH46TXrkOvKH+tAbCpiZ53/wt2Swt4PGAYOAdimC+tov/VVwksXsxll10GQEs0yad+vpbOmInHpfFf71nM8trSE3ItFUVRRjqmAFkIUQ9cDtSQK7nYBzwhpdw2eUdTFEU59Q0FqeXdLegz68Cb618+eJPdiXC02caOI/n+X3axrimKLSWZmtOJJUwqyvxY219FptPoeX6q62spmPFZrL1N6JWV2B0dTPnRd5jnVLOuoo5MgQe3leXMZCdLNvyVing3Mw60ot/xr7nv9aBNenpVFXZrK7K3l/5vfxtRXoEIFOTeTLS05GqdvR4Kbr+dDT98hE5/MbHqANnnnuMj9fUYhotntnXwrSe3M2Ba+N0633nfUk6bVnTCrqWiKMpIEw6QhRD3ATcwVFj2mjuFEPdLKT89KSdTFEV5AxgKUhtaWli2O8u65hgO1iGb7E7k6x9cVjGUzd2lBVjXFKUoz40QAikd4qksLY1tuPGg5flYflY9c6eHEJkAxsyZ2JEIIhjE6uhEr1sIQstNrrAtilL9nNGymX3FYV6cthj5798jcM3VhAsMyiMRXKEQVqSVgk/fiMxmGbjnHkRhEdnVqxElxciOTmQyCW43xT/+CT8xy3jlHAMnkyHfm2VmfiFC0/n6H7fw583tuTppAW+qK2Ph1MITeh0VRVFGEkPdwuO6sxCfAb4D/AG4C9g+eNM84GbgSuAmKeW9k3zO49bQ0CDXrl37eh9DUZTX0UQmPhyL4SkWY2yym9Djj2MKhhOL5WYYR6P8fdaZ/LHuzYSCr2Wxu7pjLN/yN6rckrKeCA3f+AKeWbOA3PXJNu4l9q1vsX3rfn50xnsIWkmEbSOFoM8XpNDspzW/lITbT8Zw4bezhMiwKNPF+7b/Cbq70UtLsbu7cVIphOMgASFlLnNsGJQ8+FP21i3lrie2UqolsPGB10M0aXPmrFJ+/sJeHAmGYzMnG8WeVsOtV8w/YaUqiqL8Uxvzl+xEM8gfBf5PSvmug77+MnCVEOIp4GPAKRcgK4ryz20iEx+O1dE22R2JnUpx/+Ob2BC1cYQ4pikY0jTp/cQNZFavRisooNy/F1G7AinlYAZZonvcnOZKQWsbHdWz2KMHmetINE0gvF6Ex40TaaWjpBJH0xG2DY4DQtCVV0xLcRhhW2Q1HcO2yBhuXMkYW6sX0PGmeqp++B2Ez4+zezeuBfOxOjqgN4pMpUAIih+4H+8F59O6pZ3AQCN5if0MTHszUjcYSJv88u9NOBJcjkVduhdvOsmBdOaE1nIriqIcbKIBci3wwBFufxK4+9iPoyiKcmJMZOLDySZNk9U3f401vnkU6Q7u+fVIoU14CoYVieBEo2gFBTj9/czyZmmYVT5Y9pGriV5Slc+L28rYUjsP6fXi+tNuGmr7hgNxIxxGr6mh4tUWNMdGOg5CaCTcPtKGG8e2kUIDBJbuQjo2abcPw+2mp7qa6qoqzBdfAssiu249DP0rpRAU3XsPvssuBaCq2M9A3nS8/gBORtJnpmiNppCAoQnq0lG86SR4vege9wmt5VYURTnYRAPkA+Qa8w5nBhA/9uMoiqKcGOOZ+PB6sSIRWmNpZIELEgew29rRK8onPAXDCIcxqquxAGPuXEq+/wA3FASGyz4q/ToH/uNOvu+vpzB5AM32YGiVrGns4UkngacoyBSZYt7dd7HkuRdY8Ps1bCmsxnEkibwALtvCNNy5cgmRC3xtoeE4EpHJUFnoJf+GGzCffwEO2qKnTZmCa9Eistksa9asYdmy5Zw2rZRXnm+l32ml01sIQhAu9nH6jBJejeRhpjPoHjcNtaUnvJZbURRlpIkGyP8HfFII8aKU8o8jbxBCvB24Hnh0sg6nKIoyWY428eFkOFwNtBEOUxX0ILIZ7GQCZ9cusk1NaAuWTihzKrxeCu+9h22bGulwBwgPQF0Bw2Uf2T17+J+0wCkwIGVBfgHC46FjTwc/3WfiNxPohs4i5xk+eGAz71u7msbCKnrql6CvuJAHX2kj4y/MZZAlIAQCm6QtWbz575Rt/y3OtVdDJjP6YB4ProULMcJhtu3cycqVKymTko9U+HF2vcJv5l8MQjA9YHD/vy6nOM99XLXciqIox2uiAfIXgTcDvxdC7AF2kPs1OReYCbQM3mdchBD55Jr7lgINQAXwcynlB8e4rwF8AfhXYAq50XL3Aw/IiXQaKoryT2usiQ8ny5FqoIXXy7Jv386q/36adek0jq6j2Tanu1MTypw6juQHLzSzrqkfW8bRReuoOmYjHKaq0Itu6IiCAowZ04nHE6TQqbRS+AeiiECAjSLIzh6T2bNnMae7mzOvvxpbwqonnmfVtCUIx0ZqAq+08Vhprtm+kotKHKzmDqIf/ihks68dKj+fkl/8HNfCBbzam6YtE+DMXpP8O7/JH8KL+c38i5FCo6avlXuvaKC0IDcm71hruRVFUSbDRDfptQkhFgO3kZuDfNHgTfvI1R7/p5QyOoGnLAVuB9qBtcBlR7jvD4CPAD8GVg++9veAYuCOCbymoijKSXe0Gmjd5+PGD53PS+/8IB3CS4U0Oes/fjahzOmO9vhBY91Gb/MTXi+n33kbC7/8EzaJQpyd+0jmB/EJA388mlsRPTCALKmgXbqpWfd30DQOfOe75F99Fdev+S3SkeysmIkuJfnVlSytKOOiXhe0t+dawUcEx6K0FGPKFNL5edzzk0fYq03HzGpI91zy5texyZdbFz073sYdqbWE6v9lsi63oijKcZnwHOTBAPjWwT/Hqx0ISylbBzPE2bHuJIRYRC44vkdKedPgl38ihPgd8EUhxI+llO2TcB5FUZQT4mg10I4j2ZUQpL55N7Pam1hwXgNG4fhm/w6VbkTibuzBiRXAmNv8ZE8P7331/6h0VbG3sIqCuMG6urMQMQ/C48FJmRhFRVRg5jbdaRrZ1auJR1pwed186oWfsbd0Gu3FlXhXXI9vdg3dZ9xJ7UAH8S9+ieHKYyHQy0Lo1dVszxgkD3RTVDYFOxBif5ePPa5cZnxxuID/XHE2gZr3vC5lL4qiKGN5XVdNSynTQOs47nrt4N8Hj4+7F7gKeAe5DLOiKMpJN575ykeqgT54450u8li6upPrLwweNYM8snSjoGY+2vzLR411O3ibn1ZVxSOLLmdj1o8jNHS/H1cwn75YMU4iiaZ7WdS2g5rILkinQQjI86OFw+zMr6CjME1ZrJu9gQq2PbcTvR28wuH6P9xNaMdGAHzvvJLArbeQTSbxTpvGwO4+mkvPpiTPT0tvkshgcDwzlMc9H1iO160f738CRVGUSXXEAFkI8f7BD38ppZQjPj8iKeUvjvtkozUAnVLK/Qd9fTXgkKthVhRFOekmMl/5cDXQRyuNOJKRpRs1TVtZ/KZ3sKEvMzzW7eBtfjujGbbNaqDMSSMzGbSiQnpNhxWLptHzl78xU0uxYsuziGQCBr8PY8FCHvLWsaZuGmkr1/KR1l3U9u7Hu2sLV29+itCezQAUfvMb5L3/OpLJJL/43/9l6aJFlIsCkIJ9Xf20xdIA5HsMbrmiXgXHiqKcko6WQf4ZuSa83wCZwc+PRgKTHSBXMkamWUqZEUL0AlVjPUgI8TFyi0uorq6e5CMpiqJMznzl1mjyqKURhzOydMMVDvPJKxaxM5o57ASIlt4EyYxNtqMLTzpJXkcHXcXVPNUvEOVz2JgYoCmc5rq2x9B9XtA0Wv/ts/xtdZyMJXESCbK6gS104t4A73nx10zr3AtA+w23UPX+6wDweDyUFhXheuTXlDbuJbvofbS5iwDItzNcvjDMgrBaH60oyqnpaAFyDeQC0ZGfvw58HH6+sjl4+yGklD8CfgS5VdMn5miKovwzm4z5ylXFfvTBzPHhSiMOZ6zSjXlVvjEDa8eRrNrdQ2cshdB8aH4/HitDPJXF0AWGOw9HGjw/+0zOat1CnZbAVTeHTRSSzMbwJPrJ6C4sTcdwbK5d/zi1g8Hx46dfwVnnnEO8uxtvMIjb7ebtS5fS8/OHeaDuUvYOBscLUp28v+k5ln3gNjW6TVGUU9YRA+SDSxrGKHE4WVKA5zC3eQdvVxRFOemE10vhf9xB6k9/xnfxRUdtNHMcmZvxG01SVZzL8NZNCbC0ppi1e6OHLY042hkOzlqPrIuGXKZ7lxagqTNOkdlP3NGRQNzw4UjQ0yaOlGiOTcJwse30CzjrY5fgqq3F+fs+bNvG1A2yugvDsfjcsz9kaWQLAH9Yehn9Lj9FX76Jh85ZQdncOt7z3vfClErumf8OnvflznDpwB4++urTuMJh3FOnTvBKK4qinDwTatITQuwFPiOlfPwwt18G3CelnDEZhxuhDVgwxuu5gZLB2xVFUU46Jxaj65JLcWIx+r97L+XP/gUtOHZZxKHNeGJ4TvH1F84etfFuph1DZNLDdcBDxtMQKE2Tnhs/w66+DB3l06i0E9Ts387Omctxas4m3N9JwhKYhovuvBIGdANTGCAkCAOEwPe2t+Kur8dOpdj99N+QvqlkdDe6tPnsX3/MsuZNADzccCVJt5+PvPQImtvF0r17mXrZpWQshy8/vnM4OH7v8jA3vPlN2K0Xv26LWhRFUcZrolMspgP5R7g9H5h2zKc5vHXAW4QQ1VLK5hFfPx3QBm9XFEU56dJr1+HEYmh5eTixGOm16/BdcP6Y9z1aM968qiBzSzxEb/wMsRFNf9LtYUd7nEhnjIKffp+apq24jtAQmGlp4SFZzZba6diZLBqSRbMrOaN1M9q0M0E38JsJ/JbJQEExSTG4GE8KJKBJSWEqBsDWtTtoTuuU2FH6fAFufP4hzti/AYDHFr2N/53/Fq7c/jTdZSHKu7qYNZAgUL+AWx5ZzyuNvQB85LxaPnxeLUIItNdpUYuiKMpEHMuYtyPV8s4G+o/xLEfyW3LLSW4kt3lvyI3kmgf/ONaDFEVRTjRPw1K0YDAXJAeDeBoOP1RnPM14Bzf9ZVpa+HGTw7qmKFbKRPrmsWh2Bdft+suohkBpmmQbGwHBbiPIltIagskYwpOrTtvoFHNmcTFz1z3LlqLpOL4grmnTmFkawGyNY2YkMptFlw7ebBpXf266ROuBNFJKqvta+ehLv2JJ6zYAHl18Gf+z8K34rDSiys1zNedx1e//H+7P38ZNv9vGhv19AHzyLbN534rR7SvSNMm0tLBHD9KWsIdLTVRNsqIop4qjBshCiA8AHxjxpS8LIT46xl2LyJVBPD2RAwghPgkUkssEAywUQnx58OPHpZSbpZQbhBAPAjcJIQp4bZPeNcDXpJSqxEJRlNeFFgxS/uxfSK9dNxwsH854mvEObvrbowdZ19RIUZ4bfDp9HTov6FOonnUmV1VV4TiSV/d1s/OeH1Cy/mVq+yLsPP8qRN35uI1KAITPi9aX5EBhCe/99xvYG5pGZ34p08/+KL9Oa6SyzmBwKsnLpCjMJKhZdnbuzIUedBwu2/YXFrTvBOB3p13KY6ddhi4glO1n9rZGegqK2XXR1fxqq8P2tlz2+ea3zeWq5aMnCA2Vfzwkq9lSWoOYOhVd10etxFYURXm9jSeDXAzMGvxYAhXAwZ0jEhgAfgV8YYJnuJnRZRmLB/8ARIDNgx9/HGgGPgR8kNx660+TWzetKIryutGCwcOWVYw0nma8g6dSrN/dhy1z/3C3P2pyoKCcrG3zaMUKup5vBiRrd7STdU9HLKlkQeduzmzchqhdQbalBUwTvF706bOYWp2Hpglmdu9jRm8LP+73sr69D6QkaztoQqPfm8/pi2cwb1YuuJ5bW87H1jxG7WBw/MeFF/OXs65krtsk6ETZZgV4cPn70LNpOoLlpNv6EcAX317P5UsOnehhRSLs6suwpXY6wWQMtxEGv3vcc58VRVFOhqMGyFLKe4B7AIQQDvApKeUjk3UAKeX0cd4vC3xt8I+iKMobjqaJUc14Y80phtFTKYayzv1mlgPJDC5DgDAoLfDw4q5uACqL8rBaHWyzny2hWpZHtlDfuIFNnjLwBcHKsqxYZ/6ZCzhw/vlkd++muX4ZW2MSLW3itTNYDmQNFwEzwbJAMfbeRpz8fLrfdB61qdygoM3TF/HsaRcxe2oR3gO70eNdWEYRmmnS5S8mbQNS8t6+zVzQKXFiBYdk1I1wmM4pNdiWjeb1InxemMDcZ0VRlJNhQjXIUkrt6PdSFEVRDkfTxHBD3sHGGgE3p9jNaYWC51pNsrYEBIV+FwU+F939ua10QjdwLZiP1tOL09JJ34IGrtvzLCsqZ9Ge0agMelh2xdXoPh/FP3gAKxJhU6eNeHwDQvMgHYkhHRxHR8eh8LFHiHY1Ybe3Q+q1KZqBKWX4silkyiRdXEdUryTbm6XD5SdjS4R0CA1EKdv4Mn1P/hCtuJjyZ/8CBYFR39esT38M15PbMYryQNMnNPdZURTlZDiWJj1FURRlktmpFPc/vokNURtHCHQhWDI1yLv/eB/XRiJUzDqTR6evoLTAQ4HPhZTgMXKNdFJKhKajlRSjdx2gvKUVd3U1Z9x1B3Z39/As5OyePRjhMFpVFX3f/iZZ/2w8GqRdHhwMHE1nrtVHTUcjsq8P2Z3LUKProOv4ZT9z/C00ZcJkU3GSUmA5EkfmmkhmRJuRCMrj3eD348RipNas5SEnPGq03ZLpxTTUTWFdUx8O1oTnPiuKopxoEw6QhRCzgc+QG7FWxGvNdUOklLJ2Es6mKIpyyhsr6zvRRjNpmqy++Wus8c2jSHdw19fTn7F5dmsbJXEPF5YUc+7uVXScdQEb+iTd/Wk0BGfVFuH0HWBDdwzpdqNpOsvPmsdpF4Twnd6AFgyiBYNI0yR642ewIxFE5RR+ufRdvFgwh34jD0vTcUubQCbJ3I7dXL/1CYTXjd3aCoDvnVfiOv10fG8+D7O5mbLNm1mxfSW7Bwweq1qGo3vQBFQWeiETYGHHLmb0d4AArbCQpql1rPtb86jRduuaotx86VwumD/liKUmiqIor5eJLgppAP4GJMhNklgKPAv4geXAVmD95B5RURTl1DS0+GNtYw92OoPucdNQWzrhaQxWJEJrLI0scCFTMZo6YsQyDtl0lkerltEc2cyHKz188opF7Gjvp2VvG+GpIcr+66tkVr3M4qIw0SVnMOuTH6H87q+TiEQwR8xJtiIR7JZmhM/P1u0t/Lk0QcaVB0JDE4Dbw1VrHuOCzm1woA/HtgHwX301diJB9LHfk//3F5lx33f5UFkZ6/9yJ4/NvZK47iHfrfHpS+bidetU+ucy027A+NYnyGzZiqdhKVsaY1gpE+E3QOjDo+3aYyneMn+KqjlWFOWUNNEM8teBTl5b0NEFfENK+awQ4jzgcXLZZUVRlH94O9rjrG3soWDf7uFpEWuBHfUVowK/o2WZjXCYqgIXIpMm4c3nQDyFYWeRCIo1my1Vc2m7chGlQlDxn18hFIlAIIDT0oywLWZFm9G3ZwlEzqF/xAzloTnJeiiE3dWN09fH5vq3kHR5cdlZhMeDbuhkLYdYYRnsehGGguNrrsZ94YXs+tGPeWrxIi7esQv/iy/RWDWHL817FwnNTcBOc997z6RuemjEVSkDwHfB+UjTpOCn30f65pFpd3DPr0cKTdUbK4pyyptogHwGcJeUMiqEKB78mgYgpfybEOJn5ILoFZN3REVRlFNTazSJnc6AaSLcbqRpYqczo6YxHGm99MgguTbTx4J0My+Vz8NCgG4QSPaTl+0nk19CV17J8BIRrbiYzLp1SMcB00T6/Rg1NbiWLqGxZj6tsTRVNeUsq6oCwO7uRguFENOq6fWGyKJhGR6EA2RtcCSnFQqwLAB873gHVixO6uZbKMxkmBkooHjfPlb99yN8ve4dpDQ3+S6NW96+iNnVpYe9PlYkQk3TVhbNrmAjxWi9A+her6o3VhTllDfRANlNLoMMMNTaPPLfxzaTm1OsKIryD6+q2I/ucYPXixyaN+xxMyXoY3trjNZokozlsHZvL8X5HoR0cJIp1jb2jMoyW5EItLfzryGLqS0xHp16BsXpfvIMgWtWPS5cVBZ6kal+tClTsJuaQAg8DUux29opuPUWPOedyw9eaGbt/MuHyz3WvdDM9RfOzjXmVVfzkKxmY9kcpKYNrpYGHMl71v+RGRv+FwBRWIjdFyWSSBACPLrOm3buZFNVPd+ou5KMZmAIKPHp/OqVCDt7zEOCfWmaWIOZbFc4zHW7/sKKmvkMvOk8qsqDqt5YUZRT3kQD5FYGl3pIKVNCiA5ySz1+P3j7bHL1yYqiTLLJaAZTJlfdlABLZ5Tw97RFOmni8Xs5u6aEv27vYP2+3IKPAdMilbEo9htkt23PrYT2F9LaOXU4QB65Pe8tYQ+9Z81h7d5e4i4XGoLFQQjd+SUOtLejV06h8O5vM/DAA9ht7RizZuF783m82ptmXVOU4gIvIuBDSjlq+Ubn5/6dbY9vJej1Eo2mcRyJBN696UneNRgco2lgGPQlEjyx+DSWSMniDRtZM2Mpdy24CkvoGI7NvEwPnr7kmCUlIxsC9XCYoru+hd3dTXk4jPB6X5//UIqiKBM00QD5BeCtwFcGP3+M3PrnNKADnwR+N3nHUxQFxv/P9MrJJ20bp6sLx5E4A3F6KwvYHzVzGWMh8BoZdnWk6Y8l8ZomuN2QzVCeiQ8/x8Hb8z7uODwlTHb1mlSvfY4V25/Dau/AWLKEHTGbeI+k+ot3MtOO4Z46FeH10hrNBeRC5FZGJ9IWsVSGV3Z0MKMvwq4fPkbWMwOvLnC5gmiOzWVbVnLV+scB6PcHKDAHkNEoRXNmc2lNDaHnXuCV86/m7urzsYVOcTZBnjmAu78HgkEYo6RkqAxkqA7a7u4eXnqiKIryRjHRAPke4GIhhE9KmQK+BNSS224ngWeAz03uERVF2dEeZ11TdNSoLLWad3IcT2Z+R3ucdbu7KE/0orndOKkMm/cX4/K4KRG55yjwufC5dXqz4PMXQjbDaTJG/aLR0zCHtufZqRTf/fKP2Zj14yDYSAXN9ZdwbfvP+aV7JlunzkLsTqDvbcy9Sar1gGnmziAlUjrs70lyIJHBciRPPbOJro4dLN/5CtqKWfgSfQTccO6ul7hmwxMAROpPp3LHBnbNrCXU00uZI5n6m0dZWTCDB6ovwNE0asxe3vfy73hkwdtwbBvdNCEvD93jptKvD89YHpkN1wc/VxRFeaOZ6Ca9reRGuQ193g9cKoQIAraUcmCSz6coCoPNYMPZQYZHZanVvMfneDPzkc4YjiMRHg8ynUbzenG5XdiOzC3vEAIpoTzg4+rl1bjlDMozceoX1aL7fGM+57ZNjWwiSKEZBSkB2BgoofqCq9leezahQj9ksuDVWbs3yqv7uqn4z69QFolQP/di1lYvpCduYug6JV6Nsp4oW0IzWbZzFQu697CltJZLNj/D5VtWApBeuoyFX/syne//IGuXnU5VezsXNEf48/zzeaDkdABmmz18ee0v8bXtY3P5LLaWz0YLh9EDBSydVkTZf36FvsGAuPi+747KhquyCkVR3ogmOgd5ppRyz8Ffl1LGJu9IiqIcrKrYjz6YOR7KIKtRWcfveDLzI0eYoYE+fTpaUSF+UzI9lMc/t1S3AAAgAElEQVS+7gQOcnhL3CWLKscVdLe7Ckh583AsG4+VIT/Pg1ZZTaR2KTISI7t9O3IggcjPw5k+m5a9bYQiEVyhEO/b9jQ+M8Gf82dQ4pgEp1XheDw4lkVv7Txu/NhFRJ55Ef9gcCxCIaY//BDC7aagoYHLVz6D3zT549zzeWgwOK5Pd/PlrY/h7evBMXSu2/Qk+8+5mPjC06ieU81MO0ZsjNFyqqxCUZQ3somWWOwSQqwGfgU8KqXsOgFnUhTlIHVTAiytKWbt3uiooEuNyjo+ozLzjg0pE8cR48rMD48wm1XGRieA09yB3tnH8jct4hMX17Gro3/CW+IcR7KqMUqPN4DuDSA0QdDvJuDzUDe1mM2NXdg9vQgpcUwTMSXN1Bmzh0sajOJiFrfvYFU4hK+/l2xfNwQLSTs6rbE0Oz9xE6GuFgDcy5dT8tBPeWnzZnRdZ+nNnyP7qRt5bO6FPFy6GIDFZid333IZYlMl/d/+Nq7SUuxIhFn9HfC9r6KHw7ju+pYqqVAU5R/ORAPkrwLvBu4F7hZCPAM8AvxBlVcoyomjaYLrL5ydq5VVq3knzXBm3rawtm3HMU0cfyGV/tlHfawRDuMKh3nv5idZFnPoLKmkPNnH8vd9G0PXmFcVHDPIHhqBNlR+IE2TbONeQLLLKGTPhh0UORpxlx/p8dCbyLIwHOTNRpQd2V7WewM4QqBJSYMvy9zpIcRgSYMeCjHr+huY3/oqWytmYWs6fUYQ4TgI23otOD7nHEp/9iBSSlp378bIz2fZpYv41eIr+F3+HACWJSLc+urjuA6chXH2WSQfrc4FwZWV2L1RjPLy4SY8VVKhKMo/GiEHa9wm9CAhlgD/AlwDhMnNRH6cXLD8tJTSmsxDToaGhga5du3a1/sYiqIcp4MDzOMxVIO85tU2si0RNENnYe8+Pn3r1XhmzTrqa0rTJPXXv3HgpsHeZCEo/d2juOvrxzyzHgrRd8vnR41Ai950M5lVqwBYdfYV/D4wlyKRoTcDSU8e0uXig7EtnPnXx7Al7PWH6CwopdxJsfyXD7CjsYtIay/Tly1g3qxK6I/T/W8fZ0dzH5vK5/D8nLO4aP3TXLrtGQD2TJ1LyYM/Zk51CfGbbibT2opWVcXDb/sEv9vQAcCKZAs3bX8cT1Xl8Lrqw30PQ7criqK8QY2ZaZpoBhkAKeV6YD1w8+CK6X8B3kkuYI4CocM/WlEU5dgcPGP3eIMzkUnz0RqN86bNZvcDf6dsfxOzi9y4p04d12sKrxffm88jdfZZWE1NuW12taOnU4x8vFZUOCr7ml67Lrf0w3EAKIvsQSyYR8Tx0+/3IoXAFhqrTS/LLRtNwMx4GzP7WnBcbu75wg/ZUjiduDePzNZVzJtdyZ0rSgj98L8pikRINqeQv3tyODjeXxzmp8uu5owHf8rL1aVc0taGqzTE9/RZrBwMjs+PN3L759+FNnDeqDcEQ1M2AJUxVhTlH94xBcgjDa6Y3gJsA+4Aio/yEEVRlGNy8IzdoYawYzEycJ0SDjNvcKHFwUHf0V5TeL0U3/+9wwaMox7f2YlWXIzd3Y0eDuNpWIpeU4Pd3o5MJqnZt51wxWJWVc5Hcyw0oDAZo8VTRKOvlJnR5uHn3VtYxZbC6bQHSjFdXiTw8v4YH3y1ifvjL1B233eZ+eJvmf+nBwFoKQnz6JK3YyMo27UTLRtHL6/gbt98nivJlVVcvO8VPvLir4h3vkTJDx447Pc0Mlg+3LVVAbSiKG9kxxwgCyH8wJXksscXDj7XLnINfIqiKJNuMmfsjnehxfG+5qjHV1cPb5YbCh4L776Ljb9+kqZnX6TCTnHavo3sLgzjzaRwO1mQ0OsLsrFqHg6CrmCIilgX7XmlxH35mC4vmnQQgIOgzVfIMx15XPajH+P/r7sAyb7wHH56yceRfV3M37+V5VvXo2Vm8Z133sbzrSkA3t61ietefBg9Px+rpYWe6z4A2Qz61OoJZeonO8uvKIryepjomDcDuIRcUHw54AfagfuBXw2WXiiKokyqkcs8Km+7Y9QGuYkYmdkcb+B78Ja7g19zPAFhwadvBCSu2lqE14vweLAiEURpKffc8XM2yiCpyjOwNZ3KYDuedJKglaQtP0S/O4+spvP4/LfwxPy34MuauO0MM7r3k3F5kEIgpBj8WyKkg9PVSf+3fps7fyCAqKtjadOzRKtLefvOlWR1g7vnX8W6weD43f07uLbxWaTfj2OaEI3i7NuHVlICkkOy5kfKEE9mll9RFOX1MtEMcidQCPQDj5JryntWHkunn6IoyjgcdplHrWfszorDGCuQHW8t7ZFKCrKNe7H27MlNdzgoIBzrNUd+bXfZdDYULKLP4yXh8wOSqD9AeX8Pnb5S+rwBdCDfTNDvzcfWdLK6C13abK7Ko+JAB/HQDJzBCyGkwxn71nPutude+74HBpi+9RVC/XGa2qfjSmX45js+z5a8KgD+bXExl/5oJfqUKWQzGWQygeM4kE7jxOMYdXOQ6QzSNIeb9Y70hkBt0lMU5R/BRAPk58iVUDwppUyfgPMoiqKMMllrtocymyIUYkdfhvgLrzJt3gzqZtQijnFcnjRN+u+5h2xHJzvSLnoWn0GdFmCuI9E0QaalhR19GTqnL6W8u4WGlhY0IbCamxF+P5FYms7yEpJuLwKJkLkgN627WNy+kw3VCymN9xBz+TmgBUGCjoPhWKTcPhY3byHh8tJWWIkEljdv5sbnHkQDJCCFYM/MWma1d+BPppi+u4n/uOQmdgZzTYg3XVLH1aeVE30qjNXcjFZejvC4ya5dh9R13AsWgNfHgVtuHQ6Gx1WTrZr4FEV5gxt3gCyE8AIbgX4VHCuKcrKMd8320RrDjHAYEQ7zkKxmS2UNYncCfe/2Q1ZLjxxnNlbT3khWJEK6rZ0H3voJthvFuIoKyf/Tbhpq+/j4BbP40e4sq2e+BStrYc2qp/rlGB88vYKirm5EdxfZmWeRdnkA0GQuqLU1PbcKJpNFS2dy26a9ufsIQBuceAGgI7n++Z/x3fP/jUVt2/nYi79Clw6twXLSuhun2M3z551L3uo1BPpNvr7s/TTmlSOA266o5+1Lc9ndoru+RfT6G3CiUbSqKkp+9TDC7QJELjgeEQyPJ0N8tCY+RVGUU924A2QppSmE+ALwyRN4HkVRlFHGs2Z7PHXAwuul67Y72PbENkJFeQjdOCQbPfQ8VnMzTnc3ellozCa1oZrolpiLxxdcxWZXCbrjQCJLf6KHF9IWYWeAdftNSutmsL9rgFhG0rUvSnNrL7WnX8tpjevpLSjFY2dIurwMhb2a45Dw+NhSOYeU7qa3eCq50DnXhmcLDVto+LMpFrS+SlcgxOn7N/LB1b9Dlw4dBSHuuuATTO1r47Itf+aSJ/4Xv2PwlYs/Q7M7iCYdvnRGKW+rLx3+fuzubpy+A+hl5dht7WgF+bhmzkSa5iHBsMoQK4ryz2CiJRZbgekn4ByKoihjGs+a7fE2hrUlbKTHg9Bzv/oOzkYPPY+Wn4+1Zw9GTc0hzzeyJjqRtmjPq0Q6EsfKYmk6aSDZO8BvV+4Dt4fEjGnEMhIXDk4mTSyZ5aXS2ezylyGkxHAsCtMDJFxecByk0NAch7L+HrJBFwmPH1s3wJGDNdeS4mSMpd27mRFtIeXy8P41j6FLh7ZAObdfehNT8g6wITCfNdULmdXZRF9RGe3uIIaAW3tf5vT71xH942tvJMbKCjuOZEdvmsjHv0hFJk79otoxZyIriqL8I5pogHw78AshxBNSytUn4kCKopxcIydEVBWfeiusx7Nme7yNYUfLRg89j9XcjBYMIgcG0KurRz3fyJpoR0o0ARmZK43QpYONQDg2A8KF27JJ9saQZhZpZXGETkYYaI6DVxeUhorZ2ZPEn07gMxNYmo4/k6Lf7afPX0jMV4DUNJASt5NFSPBm01y56WnOiWzir3NXcN6rL+BybCLBCr56yU1k/W6miCi9RpAOWcL2qjoQAred5bb9K2no2oVeUTEq8D84KyzdnkMbI+PNo0pRRjrVf4YURVEmaqIB8ruBLmCVEGID0ERuzfRIUkr5gck4nKIoJ9ZhJ0QcJhB6vWiaYF5V8LBNeeP9Z/+jZaNHPs9YNciOI3l5dzddcZNE2sJtaOiaQBMSBw1HaCAd8jMp/GaS0nScNgkZT4Cs7ka3s1iagRA6TjqDE2mhyNY5a986yvq7WR9ewJ7QdPp9Afp9ATK6AUIAAkt3oTkOhmNjOBZbiqdz7o6/43JsevyFPHDOdcR8AZDwnDyNrNQHL44AKXnbnhdYtOYprPx8EOKQwH9kVvjV1hjrmqIU+l0kzCypRIoXdnRy3rxy5ocLR13TN8rPkKIoykRMNEB+34iPlwz+OZgEVICsKG8AkzUhYrKdqIzkeLLRIwNFLfjaNXAcyQMrd/LEhlb6UxbRgQyGLnALGHq4oQny0ybT0lFivnyubtvMU3mn0+vKI2N4yLpe+5XbHKwglopTmIpTnOijo6CMVytm4c8kyc8kibv9ILTcnQcnaVqaTlYz2FFWy/vX/B63bdFeEOJ7532IyuI4CdFLK6U4jA6OkZK0I9ACAUQoRMEtN+M+60xe7U3TGu075Bq3RpNYjkNzT4K+aBwciaXp/Oyvu/mv9zaMul6n6s+QoijK8ZhQgCyl1E7UQRRFOfnGOyHiZDqWjOREtreJTJpZqW7mzpxYg9mO9jgv7urBtiUuDWwJWcsGx2F+9x50Q6cjUIZ3Sjk9xQGmlubTmtrHRk95buTawd+nptOXV4ilGzxVfwHtgTJSLi+GLMJlZfDYWWzdQCJACJzBCuSqWAfXrf1/eOwsHQWlfOvCj9OXX0KxbiJHToYeCo4HP86GytHi5RjV1biWLuF7/7ORVZ0Z0mYWtw4r5k7hk5fMQ9MEVcV+spbkQCKNYVsITSAdaOnqPyTwbelNkEhb2I7E69bJ9xiv+8+QoijK8TrmVdOKorzxjWdCxMl2LBnJ8TbpHc8a5EhnDDNpIs0UPtvC1nQyaORZac7d/jxnd2xjT2k1T7z/C7QJD92JLA8H59Mv3LjtDIdsNZESzbFJunzYuo7UNISUOAI06WBpOprjIKSDjsRBUNvTzBdW3o/XytCdV8ydb72RqK8QWzNY49SRbyYgTwNGBMe5FyN44Zsp/uzl6KEQq774Tf4vcDoZ9MH5GJKnXhrgzXWlLKgtp25KgHCxj/YDSeRgg2CQLB6fe1Tgmys56aErbqILAQKCPjcFXuN1/RlSFEU5XseUERZC1AohPiqE+JIQYvrg19xCiGohhHsyD6goyokzVJMbHcjQ3W8SHcgcMiHiZDtSVvtwhpv0uruP2KQ3ViA9HtI0Kfjp9zG6O8GykEKg2xa6beFP9WNpOi9NmU+rt4iuplYqg25KDYk3a+Joeq42+eAIWQjkYEY85i3ARmBIG4mGaXiwNR1P1sQWOrbQqO3Zx5f+fC8+K013XjFfveQmaoLdnCb24Muk8KdTxP3BUWUVhmPhzZp4DI1500O4Zs7E7u5mvekhqXtA5obLCSlJorN6czOQK0X54JtqmVLoJ1xRxMwpQWbMnoqm6aMC3x3tcZq6ExTnuXPfj5T0DqSpKct/XX+GFEVRjteEMsgi93+s+4BPwPCyplXAPsBDbgzcV4HvTOYhFUU5McZTk3uyHUtWe7xNese6BtmKRKhp2sqymWU8o00lpXvBMPBbaXx2hqfqL0AKQcKbR1r3EUqmEEBQs2mXDlnN4LVZxqNOjq3rxD35OIPNeLmmPIkjNNIuD1LXmNbVzBf//D38WZOevCJuv+RzdAZCuCS4s2mm9rWxrTI3rcJlZdHsLGmXN5eR1jRqQ3lcNKeI7J496KEQIhDEEhqWyzvqLKKgYPizeVVBVswJsXZvFBONTMo+5M1TazSJg6SmLJ8B08LM2qQyNmfNCo37Z0hNwFAU5VQ00RKLzwE3APcAfwaeHrpBStkvhPgD8HZUgKwobxhHmxBxso1n7vFYxjOb91iXXBjhMK5wmPfveZazquex44Ir0YJBPP/vN/zeMwOP7uDJmNjYRF1e2ve3U9LfS29BGZomsB3IBceD6/IGs7yGbZHVDaQQCCmHR7o5Qst9TRjM7NrLv//pPvKyJr3+Qr5+6adJ5PtBQIdVRF3vbraG5w0+pwPSwWNnmde5hynJKDNibbzznTcS/8xNw28Mqj/wOcTKvTgy12A49HeoOH/4ex7Pm6fX3sxAvtdFnscgOpAhXDK+8go1AUNRlFPVRAPkjwC/llJ+TghRMsbtW4CLjv9YiqL8szrRWe2RgfR4s5cjA+uyUIjl3d1oVVXc2n4pPXt6MBybjKYjNQOJQ0TLpzvPRVa4CCRiJNy+3AY8TUdzbBzNwGNlKEgPMOD2kzXcg5v0ZK5UQeSmUEzrbebf/3Qf+Zkkfb4gt7/tc0wNRqmlm+ftBWR1F1vC9SAEwnEIpuIEUzEs3eCa9Y8zO9oMfj+ytQ27pRk8XjJbtsCuXQR8XkxL4jgSTRN4DQ236zBVdwd3GA461jczQ9QEDEVRTlUTDZCnA3cf4fYYUHTMp1EUReHkZLUnmr0c2jg31OTXOG0ekdqLMTwuNNMeXBBiYVgWFfFOevNL8GTTeK0MpseHz22QylgI6ZAW4AjBgCefzGAG2bCyZDXP8OtNi0a4/el7BoPjALdf+jnagxXEnELcZIeD6KHMsS+TJO3y4BvIkAa6AyFm9+wDx2HgN49itUSgrw8HQfwXD+M6813khUqxpcDv0dGFYGpx3oSuz/G+mTkVp6goiqLAxAPkA0DoCLfPBTqO/TiKoignx/FMy6CkmNWdaZIFPfgMF/3ClRscgcBrpTFdPkzDgxSCtNuLo2louo50aUgpMawsupQgncHa49yM4yFTo63c/vTdBNIDxLwFfONtN2IUCpCQlB6SeF4bvixzZRumy4smHVK6G0M6lMe7c7d7vVhr1kAyiYPg4dPfyebKOg4ID9kDaQTgMgQzygqwbIeVW9qpKvbjOHJc1+d43sycilNUFEVRYOIB8p+BDwsh7j34BiFELbkSjF9OxsEURVFOpKNlL8cqvzDCYUQ4zINWJaurpxHz5KPbuUkR0vCgSUlh8gBthZXYmoYOZPTcYB8rYyMGS5B9dpapfa10BMrJ6q7BemOBLSVVsXa++vTdBM0BYt58vnrJZwkG0pSJbrqyAdK6F2comJYSHAeXtLGEjqUZJL35nNW4mhk9+0HTIBYDJ1fAsbd0Glsr63DbFoaQGJkklqZTEeul013NN/5nG26Xhi4EJflubOfEZnePt0RDURTlRJlogPwVYDWwCfg9ud/17xJCvJPc9rwB4D8m9YSKoiiTRJrmcIPemNlLKSkf6MVOFfKDF5rHLC/ouu0Otj++lYq2/diZAeIuP6bhwWNn0G2buC+Apem47SxeK03W5SGluQiYcdyOTcxbgKMZpA0Plm5gODYuxyKtuygf6OZrT91NodlPvyePr731JiLBKXRaGZpkBSndlwt6IVfLDOjkmv10zUFzHC7s3MI79z6HZui5wHho7JsQdATLcHw+nMpKGLBxZfqRQpBFI5GyKA34CBV4kVLS0psafOjY2d3JmD5xKk5RURRFgYlv0tsnhDgT+B5wM7m27E+QC5SfAT4hpWyf9FMqiqKMcKTgbGQQPHJKxcFLQubce8/o7KWU1O9ZT9kTf+LlcB2rZ15IaVkhwuUZVV7QesDEQeCtn0eNmaY/nqSzvZcL9qxi0f5NPLHgItZMOw2PZebmVjg2aC5szSChu5BA2nDRHqzILQOREk06VMY6+PKf7qMoFSfh8vHdiz5MqHSA3rSJhU4K73BwXEgGMiYJw4twHHTpUJCMo2kab/rQOygvvoa+m24Gjxtr127Iz4dkkqqKItzhMO48H8JMDi8nkboOmsDr1sGxIWXi1qG8yE9PPHNIdncyp0+calNUFEVR4Bg26UkpG4G3CSGCwGxy85AbpZQ9k304RVGUgx0pOBOZ9GE35Q3VD2vFxVh79mDvbeL6C+cOZy/LB3ope+JP6MEgkV37ybpbyDTuwHPGcoTbg4Mk0tpD3g/uQxYvId3m4J4xg4LCfFL7mvn/7L13lF1Xffb/2fuU26b3ppGmSFa1LFu2MS4YbMCmBgLkpZfkl0DMCziEkJCEkvyS0AKm2IEkNhCSkAAhCc29YHCRbNmWrK6RRpre25255ZS93z/OnasZaSRLsgwu+7PWrPHccs4+d9aSn/OdZz/Ppp4ddI4d5rW77mRXwyo8O7JWaCRCKwJpEwvzOALyWmEDodYk/Az16TE+fudNVGWn8Sybrro2Omf6mKytZcVMH0dKWsjFosntisl+pC2pmJuiL1nDRLKCWdslb8dIqTy/GFWc++LV2B3t5B98EMIQwhB782bOSWnW7d7C9pp23IpaMqkyEkLh1leR8hQpG/ydu1C5HDpZwbuveSlWPH7cdHd3/7RJnzAYDM9rzrhqWms9DTxyFtdiMBgMT8nJNtetzI6esHLabmlBNjbiPfQQAOkvf5mqr3+tOL3UuUomWlrwd+2iISOQWqE8j/z2HXhrNzCb9Zm45Tuc88vbWXdRikfrVuMdGsJFceHcAKsaUqhMis6JPl7S9RDbWs/Fs1yc0COQNlPJCnzpIBBU5OfI2Q7SilGRSfOxu75BVXaajBNnT0MHF/TuIpXP8M2KtzGVrCCdiAo8SnOzXHxkG1tXXIB0XarwmFYhAk3t3ASNVsATkw3sm/DovP56gkPdyIZ61PAwpe97L7Nf+zrvrVXs7+pi+vf/L7qmDseSNFUmuHf3EI/uHcQPLWSijHPHDrFKXUCsuf440WvSJwwGw/Od023S2wCs0Vp/f8FjLwc+BVQB/6q1/tuzu0SDwWA4ysnE2ZrOEzfliXickus/zKNjWYYb22gY72dzby+xlSuLz1d99Qa8PXvpePd7Wd+/hyeb1jDuVpEbSpMQ8JNUB90XvRGd96JSjjAEP4+amSE42I3MzCGBdz7yIy7p3sb25efSXdVKxo2Rt1wqcjMkAo+JyjoyVpzK2Qk+cefXqMlMkrccvnXl/6GkUaG2usz5Lul4GROpKgAqZMiV47s5f/IIjy7bCLEYakUn1vA0llZUuIL4uvWks2Hhs2jH7uwk7OvD7ujEveRFHPzpffRP52mui/HKy1ZjJRLFz2dtczl7Vlax/4ZfUXekm1WVLu6yZUv+Dkz6hMFgeL5zuhPkvyPyHX8fQAjRAvw3kAVGgb8WQgxorb99NhdpMBgM85xMnJ2sKU8pzT/1SLaufTWhH2BVruWiAz7Xdeij/mU3xoHaFTz00S+S+NGP2DS4h1+2X0hTephUfg6d93i0bjWgqJ8eQQAK2NaynpSf47yeJ6P0COCh9gu5be2VeNIBQKDJugmqM5NMWXHaRw9z/X3/TO3cBDnb5RuXvpOK9DRBRZJ/3/AahpINeE6Ui7xmcB8XndPA6v1bWdF/gA3N3ey59FpyShDG4lTFBJUNTWgpkajjPgvZ3Mw//LKHR9e/ljDvYcVctv2y57hM43Xtdaz9/MeesmnwVNMnTI20wWB4rnK6AnkT8NUFP7+dyIN8nta6XwjxU6JNe98+O8szGAyGxTyVODtR5fTewRke652mdk0H5PIQj7GtZ7rom1VKc+Od+7j1iQFmZ3Ow/pXYKsRGk5wdRsZjKKXwHDdqriPanTxQ3shkspy7Ol/M1taNrO/fw4u6H+PBFecXIt40QkdfobSomR2nIjPDH937jwXPscPXr34vIq9oHuvnXzreQjaWRBei3KzAZzRVxc/7PO46741c0LSPd40/zsTmtzOYquKhPcN0D04xOptHSmvJz2LeM1xVGkeUJU7qGT6Vyu5TSZ8wNdIGg+G5zOkK5GpgeMHPrwB+obXuL/z8E+CzZ2NhBoPBsBRnGg3WP5EhDEPIBYhEHKSFIij6ZvcOzvDA/jHyfkA89BBCkJE2eSkZk3Fq8zlUMkngxPAUjCUriAd50vEUlgqpyUyRIODJlnVkGlsYK60uRKyBFgItwAoDGmdGeeP2W6nKTuNLm5te+i5KlylGsuV8a9nbyDvx6H2ADANCy2YqWY4TBgjgF8vO5/JElsvO62QDcP7X/or9kx4jjW2s+sgfsGZF7XGfxdPxDJ9oCvxU6ROmRtpgMDyXOV2BPAYsAxBCpIAXA39xzPHOeOOfwWAwnApnEg3WlLLQvb14mWlkPI69bu0i32z/RIZcEIKQCCnJIQmEBQj6SuuYci1yCAI/RAvNkapl2CpAaE1VZpqklwEhGa+oZMCuI6rW04vWUJ6d4R2P/IiUn8WzbG687N1sbd7EymwvwYwmXxsrimOhQlQh1k1ojaN8QJCNJTnw5t/l8ngcv6sL3dfHOdVVdO76BZXZNyJl3XHXfqae4aczBTYb+QwGw3MZeZqvvx/4QKEY5AbAJZoaz3MO0L/UGw0Gg+E3SWc4zYaxbqYSZYyFFuOTc4vsCM1VSeK2BQLCRBJfRBYHN/BomRlG53MEnk8yP0uJN0fSy6CFJJXP0Dw9iAbGEmXMOnGiWe9iyrIzfOq2L5PyswTS4uZXvIN9rSuxlCI9F+dgdRsIWaiO1mghgaM11DkngQbQmtydd6FzOeyWFqymRrxtjxEODjHzpRvQudxx5563pUzMeoymc0zMeqfUWLdwClxbGqcy5RanwE/FQlEeLdts5DMYDM8dTnfa++fAncAPCz9/UWu9H0AIYQFvYrFgNhgMht84Smm6rHI6EiHL+h8jVlnOOa995SI7wurGMi5dVcOtTwwwM5tFIbF1SEUuTY3OMRqEaMdBCQtLhzg6xFchVdmpSHQnq8jEkmgEFMT1PKW5NJ++9Uu0TA8RCMn3N70K0ezSrMd5gmp6qlqONt5pHfmbC5NXtEIj8KWFsFySXpb1h7cXI+xKrrsOf89erNZWwv5+cg88SPrYqR0AACAASURBVPzSFy/aYPe0bClnOAU2NdIGg+G5zOk26XULIVYDa4FprfWRBU8niTbobT+L6zMYDIanxSKbwOqXInyfzSvr+a1jvLpSCq57+TlcXubzP9/6OVvKV1CXHiPlZektrydvx1BSknHiOCosNuVds+seJPCD819L5dQ0o6U1oBWZWAqAkvwcn7r1Syyf7CcUgm9f9GbuW/liLKXJioKIFfNb/ubRICRW6JP0svi2iy8skn6Wyw9uYZVOY7e0oHM5Zm+8ETU5iZqYgESC9Be/SOY/W6n8wucIR0eLaRRnYkt5OnFupkbaYDA8lzmTJr0A2LHE42ngf8/GogwGw7OP50pk17HrVFoft1lsYXrFPDqXI+zrY0NHPbXb/otY2xXsbFzNUKqcqXgZVXOTzMZS5Nw4nuWghaB1opfmmREe6LgIKwypzkySc+PMxEoQWpHMZ/jUbV+ibaKPUAhuu+Rqujo6yTrxyE4BRyfH8whRlMpaSJq9GQQuE8rit7ffyjJviu1/+0WWjefpyIwQDgziXnA+QfdhhG0jUiUEhw8z8YfXoSanjmsUPB2e7hTY1EgbDIbnKme0oU4I8UrgtUBb4aFu4Cda69vP1sIMBsOzh+dKZNdS66wucQmUOt4mMDzNyuxosUxkvqJaVFTg1FTzPnWYvY/s4BfNG3mieR0N6ZHodclKxsuqeXGmj3g4yy0veRdZLRlLVRFIi+apQfJVy7CDgE/e/mXaJ3oJheBfN7+RyZUNVNjZo8PiY8VxERFZK6TFaP1ySlIuL1tRzviqGu4Jkqgnx7F2TnD+snL+T0sLqq8PZ+VK8tsj64VIpUBrZHk5QU9P0Y6hc7mnzDheiJkCGwyGFyqn26SXJCoJuZZo90i68P1aos17twNv0lpnzvZCDQbDb47nSmTXUuvsm8gCHLUJhAEilyP1j19nsmc3VksLpR/+ULGiOhgeRlZWovr66Bzuh2yWffUdxXPEghwJ5VM+McyjVe1UzE5hWw5T8VLGSqpIx5JIrfnL279C+0QvSgi+esXvMlpaRclwloFUBVTIE4hjjaUUoRAIrZGWJJQSgWBsIk1PkKS6LLFoEv7Sj/4lHX37kFVVBH/2CWR7O8HUFGp0hKC7G1lejlVbi87lijcBpzNVNlNgg8HwQuR0J8ifB14FfAn4e631IIAQohH4Y+D6wms+eDYXaTAYfrM8VyK7llqna0vqyuKMpfMoFaJ6+9gwvJ/Wh29Fnr+JsK8P0FgtLQQ9PYjycnAdsC2Ix2gfO8L6gb082bSasWQV2VgCR4XcWbceJS3mhMt4qpLAiiLhtJB84o6vsHLsMEoIvvm695CrLWcyXQFzgpHy+gXiWEfTZCEQShUyKzRCA0Jihz6tFaXEDu1nR38S2xLUrFkBwop+ByrkwI23UNe1BaupEau5GTU4iF1Xh7Ys7M5S9Ows4egoQPEmIOzrK06VDQaDwXA8pyuQ3wJ8V2v9xwsfLAjljwoh6oDfwQhkg+F5xdPZrPXrZMl1CsF7rmhHSkHPnm5K772DTidPEAYEfX1Yq1axL17DI7/1h2T+539ZP7Ab3TPISGUrDSlB++wh3vHIj7i38xK+fcnvIFWI1pqMm8SzHdKxJLqQlxz3c/z5HV9l9chBFIJ/uPRd7K/poJo048kKRktq0EJihQFXdT1AbvUGShzBzK69PNm8lnQsRSitqHkPRaU/RzLnEuZy2MkUQahQmSyypCS6Rt+nbrA7Er0Dg1R84XOIWAyrtpbJj308EsStrUUbidXSUpwgzz9mMBgMhuM5XYGcAB46yfMPAq8/8+UYDIZnI8+VyK4TrXNtczlSCtZUr2b8exbeQ9sRlkQ0NPC913yA2364i9l0Fmo28v2ajSS8HJWZKawOwfqBvbxj9+1MllYRSJt4EOUMZ+04GoGW0T+jMT/PJ+74KmuHu9DATVe+i3vbLwWl6aMGpAVCYIUBL93/AP/fg/8Gj8Q41LqaoWQV5w7vZ7K6kbmrrsXbtYudsVoapIdVsYIwHifuZ2kVOQZVC+FMFs/XtFSVQl0dfvdOnJYWnI6Oom2i6qs3HOc3Xuoxg8FgMByP0EtuEDnBiyOP8ZDW+t0neP47QL3W+pqztL6zxubNm/Wjjz76m16GwfCcpZgO8SzarLXUprOnWqe3axeTH/owVkMDe2fhq5e/h/GswkrPEArBnJvEViEtU4NUZaeZSpTxqt13s615PVuXb8JVPlpDznZBStDghh6fuONrbBjcB8A3X/tuVEOcB8N1eMohsGwQAqlCzut5kkv6nsDyfLa3b6K3tBYdTyADn/WD+3jP5BNYy1r53lXv4fFpUEIgtWZTlcX7X7OBO/ZP8sMtPcxkfEriNpaATVUWH3zdRqxE4rQ34hkMBsMLnCX/R3a6E+QPAncJIf4W+IrWehhACFEPfAR4CXD101mlwWB4dvJs26x1ok1n0aQ4FiVUVFcijhHxTkcHdlsb+QcfYmDZeeQmptGxJAJN3omDEATSor+8npl4CVknznc2/zZ52yW0bLLyaAmIFJE4/pM7byyK429c+g4erD6P5cEIWRx0QRxbKkSqgH0NnexsXoMT+viWQ2UuTUtuCp3JsLO6jf1dW1kjenn/hXUcTNYVhf6qhlK+cfcBfrVvlMGpDLaIzt9ak+LxSZ99Ex5rqsXRz6SpkdLrr8fpaDdC2WAwGE6TkwpkIUTPEg8ngY8DHxdCpIm2mMz/nXUKuBtYfjYXaTAYDMcS9PUtuemsKJx7e5BVVVTddCOy/KioF/E4pddfT3DwIM1VKZzMLGATCIkqZBMLNBrBRLIimi3oeOG7BgFChWhpkczNcf29/8TGgT1o4Oar387dLZcSdyxm0yXolF3YkKcQEkqyGebcBFKHeE4coQLS8RIyuRmSWqOEZDhZyWqyOHV1rC0/ekOyu3+abd0TOBIsL48VBkzm81QnbcKcV4it8wj7+pDVVeQffIjgUDd2Z+cZ5yAbDAbDCxX5FM8fAg4e8/UkcH/h63HgiQU/7yi8x2AwGJ5R7JaWaNPZ6ChWSwtWbS1+Vxf+wYOEvT2EQ8Pkt2xl/APXoXO5Y97bjJpJ07rlHi7o2Y6jQ3J2DIVAaIVQqpBKAUf/+hY9F1kqAtzA40P33cx5/bsBuOUVb4X2BM3hKHkkPSVRWkXCy5L0ssTQ5C0H33LwrBiBlATSQUtBznKYdZPMuQk8N47SismPfXzRuucTOhI6BKWjybjSzB46gurrpeTmm7Bqa6PPZGAAAKupqXjzYDAYDIZT56QTZK31lb+mdRgMBsNpIeLx4qazhakNsrERUV6BOtCFLC1FTUwcF2kW9PUjSkuJtbTwzt23c0mml/+tP58nG1ZROTvOREk1GTe5QBsXAtjm2++05mP3fJPz+3YC8J0L38SD1ZtYNj1KLl5KWNjakfBzJHNzzCTLUIEmdBOAQKIKB9b4WjBYUkNgO8SCPLee+woG9RRv338XOx7Zw0AOWjuaaaxMYAlBSWmCssExprVDKC3yCi7ODtN2cAfh6ChVX70B/+BBpr/w94Q9PVhtbSaxwmAwGE6TM2rSMxgMhmeSU621FvE4TmcnflfXUbvF4CDlf/PXTH3h7zmQdxhpbuccWcY5oWL/UJq+4WlKv3kLLUPDyKEhcFwerl3NSEk1tgoZrGhCyRP8cU0ILOXz4V/czPl9O1FC8G/XvoVbG65AK9gjSgksJ3opmry0yZVUksrNElgOoSxsJJQSSymkgHjgMWfHsMIQjWQ2UcLjOZhY8xp6f7YXFQRYzl4uvPxczl9RybbuScqa64llPVqqErz2u/9ER88edHkZVm1t9Jl0dCBtCyWK2t5gMBgMp8HpNunVE7XmrQXKgRlgF3Dr/Ia9ZwohRCvwKeBlQAMwCNwJ/P9a695n8twGg+HXx5nUWhftFvMZv6tX8/23/gmP7BlA2w7W7QcQooswl0MFAZn4BsqubOO83p1UZabYUdqMG+SLx9OIog+50OQBgFQh19/7z1zcsx2AH29+JZnmMurVJAOiBmVF/6Q6KqAkl2YmVoISFrPxEiwdHQ2tqJ6bosTPMucmCNw4rla4QoFWpGUMZYfssVK0zI0hYzFUZo5tB0b42Bs3cdX6xuLGvY7MCJPfyyHPWYXyfcLR0aheuq+PcGAQu2UZ4cCgKQUxGAyG0+SUBLIQwgE+C1wHOBwfieEJIW4E/kxr7Z3dJYIQohrYAsSAm4DDwHrgD4BXCyHWaa2nz/Z5DQbDr5/TqbVeGGm2MON398AMWx/cRdnYIAKYq6zloFtJ+9wI0zLGeEkVA2V17K9ejiDaHCe1JpSyuBHPDnx8y0VohZYSqRQfvu9mLjn8GADfO//1/HDDq0gonywxsArtfUpRlkuTdpNYKiSUdlFsWyogKOQmW7EYyycH6a9sJO9YaBRCSrTWeE6MEj+LcF205yHjcbTjMDg6w5VlHms6owg3NQ16fBx/erpYKQ1L3DAYi4XBYDCcFk8pkEXU2foj4NVERSC3EG3OmyFKr9gEvJeoZnqlEOL1+nTClU+N3yGaGr9ea/3jBWvrBm4AXgH84Cyf02AwnCKnaok4FU611nqpmLf5KWnPwT2EnlcIntDksjlwFGlsptySKK1CgCqUdwCER68GNEUhq6WFVIr/e/8tXNb9CIFl8a9veCsPl58LWpDVbpS3RpRuATDtRo14xX8INWgBUmlSXoaLerbz0rnDBDNp/vmyd6Dyc6TjJSAlIZKOmQFmkuU4a9eB50M8hpzzKbn5Jia7dxavNxwdxaqrxW5rK1ZKy/LyRf5sk4dsMBgMp8+pTJDfQySO/1xr/XdLPP84cIsQ4k+BvwHeDXz7bC2wwHyM3MAxj8//PHeWz2cwGE6RM7FEnIxTrbWej3mT1VUEB6P0CnfdOiDa1Ga5e1GA0JqYlwcNSimUECghiPLalliAkAgVYukQVEjt7DhvefynXHFoKwD/s/EaxiqriWu/cIzCQbQGKbCCAEupqDKaSKA7oQfSonF6CAvNVeUem//8z5j++k2cO9LF9lQTlmsThrBxVSMfv+yl3HIwZFvPNAoLmQnZVGXR1r1z0fU6HR3IpmbC7u7jNuPN+7MNBoPBcPqcikB+L3DvCcRxEa31Z4UQVwPv4+wL5HsK378mhPgocITIB/03wMPAHWf5fAbD85qzOfE9HUvEqXCqtdZ2SwtWUyP5Bx8CYOZLN1B949fQboxAKeINdRwWMeL5DPHcHK2TfWScOHpeHJ8IrdFCoLWkPDfNOx/5ERf3PEFgWfx47dX8x8bXQajRwlogjhVoTamXI5mZoSKfZrCsnpwdw7NslHRIBTnsRJwNI12c/5F3E9u0iZovfZH3/MEH2LvvQYYTlTS3NfKit/0tViLBdSsXNwKeU+Uydefi66360hcRSqE9D6HUia/JYDAYDKfFqQjkDUSb406FHwOfOfPlLI3WeqsQ4joiQfzAgqd+CrxVax0s9T4hxO8Dvw/Q2tp6tpdlMDwnOdsT31O1RJwqUgr+8OpVx9VFCy+Pv8AyUCz8ONSNbKgnPNxNbvduvrknw227x5jDQguHnJvigoED/OH93+FwzTJ+uOnV7Ghei2+5S5w98h87QYCSgt95/Kdc3PNElFbxprfSXdqCVgKEXDw5LtRBt0/0MGPHSXpZOsYOM56sYCJZwUVHnmD9xGGaJgZonxsh87V+4v9wI+HoKGJyks7BLjp9HzFahfA8SCSWbC6cv16rqQk1OEjuwYfIb9kCWpOfmFg0RTcYDAbDmfNURSEQbYw7VQvDXOH1zwQDRNPiPwJeD/wlcAXwEyFEYqk3aK3/UWu9WWu9ubawecVgeKGzcOJbWxqnMuUWJ76ng87l8Lu6aEpZRUsEcEJLxOkwLw6vWtsAwF1P9PLwRz/N+Ic+wsSHPlIs0HA62rHb2/Cf2E4wMMgjH/0Mv3rsCJ7SJHRA0s+igUO1K+ipWcaqscP86Z038XuP/TexIF/wDC/YMqE1pdk01bPjvO+h/+Dq/b8C4LY1V7Ir1casF4uGz/PiuDi1jdIuRBDQPDNMb0UT++o7GSxvQALdNcs5Ut1K++hhpArJb9mCf/BgZIlIJMD3wXHQc3PkH912ws/F6WjH7uxETUxE5SgN9dHEOwwjoX6yybjBYDAYTplTmSAfAS4Abj6F114ALFVP/bQQQrwR+E/gPK31rsLDPxZCPAb8DHg/8OWzfV6D4fnI6Ux8T2TF0LkcYx/6CPsnPYYa21h+2WvoHs9GG9FOYIk4XRZOuoNMFh1bzcaVdbzzwD3F2LKFU2RRXs7gjIPnuIBAFBIkQmmTcRMMltXSOXYYgNaBg6yv38uRymZ8aePbDm7gkXfihNLmNbvv4Zq995N3Xe5Yeznf3fjbWH6AsKxoehwtEMTRODghbQ40rqTCmwPpkLdcSvNp2sZ7QQierGnnUO1yOudGClcoEPE41f/4DYZf/kr07CyyooLY5gsWpXMs3GAn4nEqv/A58o9uI7b5gujBRAKmpqCkBLul+Wl95gaDwWCIOBWB/DPgA0KIGxeI0+MQQqwl2qD3D2drcQv4MHBgifPfCmSIJslGIBsMp8CpboI7mRXD7+3lW7qVJztWEAYhzuAU7S3VvGhVDcuqUk/L0zxPcdKdsPAPHkGlJ3kiXsZly9dSX9iMFk2SNVZbG2F/P40qwPU9cB0ytksoLBQCHUvx2PLzuOTQNv79wjews2k1Ckks9LG1IuVnyFsxsnaM9235T67dcx8AP3jdbzNRUYlSAmXHoyY9rUFrpFYoYRWykiFugyfjUJHCCjXWTJa8HSfrJkh6WXQiwfjFV7B630NYyyPLl5qeRs3MUH/n7XhP7iS2+QJELHZcOse8SNa5XLExMNPSQupDH6S7fQNDZfU0TA9TPTJCrPz0bS0Gg8FgWMypCOTPAe8C7hNC/Anw71rrYqK+ECIGvK3wulng88/AOhtO8Lggsok4z8A5DYbnJae6Ce5km++0Vc6TNW2UZ6aR8Th2ZYru0Tne/KLlZ+Q7Xor5STe5POTzyPJytLKZfNWb2TOep294hNKbb6Lt0A6s8nLKP/NpVn/jG2we289dDefiOzGE1tgqpHJuir6yOn656kXsbFpNeXaGrJsg57iMpyppHzuMa/m8dtddvHp3tCf4rlWXcXfZRbjaRws7slVoTe3cOJ6wmI2VFJMr4qFHycwkIyU1jMwESMfBs2yklOTsGAkvi11ZSfurXkdl5RuZ+vpNbP3kFxjwBM0xzaqqGDUFIbyoFbCvb1HJx3xyh1Vbi9/Xxzd3zPDoylcQeh5W/UYuOuBzXYd+2jcnBoPB8ELnKQWy1npUCHEN8D/APwM3CiH2cTQH+Rwi3/EA8Aat9cgJD3bm7AVeK4S4WGu9ZcHjbwHiwKPPwDkNhuclJ9oEd6yoOpkVQylNvqGZGd1AIhXHsSwUwRlvzFuK5qokEsEsFnPJCtx8BhGLsTVt84Of7ibI5tDxNayvlLx963+h/+6zkM/xvpqAxIEpbl15Gcl8htLsDCkvy2SigkNVrWSdOGOpKnK2SygtAmnTV97IWx/7X67dcx+ZRIK7LngJ/7LqDdHEOLp40JorDm7h2kMP8sDyTTy8bCPTbgmhknjASKoaLSS+VjhKYasQX9qkYymkVqx/9G6q//VOJjaey7+UrmNH+3LCbA4rEefcqR4+3NtLbOXKk5Z8LHyuu209j08pysMcIp9BW0m2HZk84/QQg8FgMBzllJr0tNaPCSHWETXXvZ4oYq2MSCRvI0qv+KbW+vR2+Zw6nyOquL5TCHETcAg4lyihYpCoXc9gMJwiSyUkLETnctTPjSO1Ps6K0Vie4IdbexhJ55ECZCZDWcKjLOGedGPeQj9zY2UCNAxOZRd5mxe+pqE8jhCa/SNzhPFKdKyShCNJHxihvbEUK1lCvtfnycoVdLecw6rZWZTr0jWYRp6zhhIRUpcejWLQAIFisLSO0ZJqfGkVyqQjXrfzzqKt4mdXvpLxljocFeIhi+K4anaCzb07kGHAgc0vo9PW+IcOcSBRy0wshQxDdCEizlcaV6uoFOTIE7yk62Hax44gLcn+Wc2O5SuoyM5A4EHeZ0dNO11WOeuIfMYVX/kyu7YfZMgto2U8z+rGGFKKRQUg6RmX8P6DiHwekUhAPk+Y987qTYrBYDC8UDklgQygtU4DXyx8/VrRWj8ohNgMfBJ4K9AIjAPfA/7yGZpaGwwvSOYb6ur6+li35pXs6jwfJUTRioGAw6OzVKViTGc9tNaMz3psbK084ca8RRvulGI8HTXS15TGsGTkbX7/VSv5xt0Hip5nzwuZns3RUR2nayyHFyjS+YA0MHtgmI2ddQgpURKGrCTLh3u5cf3r2NvQSSgkaSvJaMMqqtITJIIcLZOD9FQ24gQevpss5CHDW7b9D2/a/nMAHlhxAT+rvAwZCDzpFMWxE3jYOqR+eoSh8jr8kVGsdZ1Mx1Nk7RhoiMqio8o8oUNKtY9UPh2uT/t4T1S2pzUjjW3IZa24LgjHRs2kkVaCgUzIusJn9Q+/7GFbd5pQz2CJ/qL3G2DveJ7+bApfKKTrQDweebHjcazYyW9SDAaDwXBqnLJA/k2jtd4BvOk3vQ6D4fnOvM/Vqa3lnXtuZ+TNL2W4pLpoxbh71xAKWFGbYjYfI5f3yWbyvGhF+Qm9rwv9zLP5gHyQRWtIuBYpR/DIngFWVLhHPc9K0d0zQNpKMDyXxrdTSBVGG+4EZJw4B3qnKFEOGVeSU5rPXvA2djSvQSNQlqQwNmYiVcGaoQPEvQxDZfXknVgxpu1Nj/+U33niZ8yUlnL7S67m3sQm3NBn3KqJ3lzYkOfbDmhN61gvAGJ2lmBqiqGyeoJ8lGbhS7t4XIXFhJ2k1PK549I30l/dwjue/BlOYyOr//g6fnZfD8Qt/F27UbkcKllBU/Kc4z6rhd7v3QPT3Ld7uHgDIRFYtsXM8k6U52PFXDZ31Dzt9BCDwWAwPIcEssFg+PWw0OfqtLSw4bxOzl0QNVb0Bud8cl6I1XsE1w8pu+VO9LpPL0pcmI8qW+hnznlh1MchIJvzcHfvx8fliZ7dzLWuJ1SaiekMaRkjEJJxGY+0rhDFxAiAMW0zlqrFCn1uftFbyduFjOL51wgNSuNZDtub10LTWrRlFd//hu0/562P/S8AD63dzGhjPXNBKbOyhKI4LixUhgETyQpuuuLdXLP7XioyUxzqGScdL0VLq1AWUvAra41E41iS1uoySsvi7F53CWPvuIYN53VS7ca44Mgsj+wZwA8tZKKMc8cO0RleANSd0Pv9yKHx44TzxKzHWy5pw3XkCb3kBoPBYDh9jEA2GAyLWOhzPTaHF2BVQylSwv6hNEopsMpZoaZZ3r2zmLgwb9OY32jW9Kd/VYyWi7tWQX9q7CPdhFOTkKpkAIvh6RxCeniBwhYCS0dFHFGdx9HMYaAgSjWhZRNaSwXZiOg84vg+pNfvuJ13PPrfKCF4dNlGblnzFlAQyKNpFcVjaE0syKOkzcMrzmdvw0qS+Qw5y0ZojRP6i1r5hFI4QqFDzUTvEOGAYrq2mce8BOvdWHGT5J7OSvbf8CvqjnSzqtLFXbYMOHEMH5rFwlkrwlwOV4e8fP0zl398NmvJDQaD4bmCEcgGg+E4RDxejBY7lkgYa1bWpcjP5bCGRsmHiiNt62ksJC4sjCML+/roDKeL0XKhVriWJAhCRkUct6yO9tHDdLecQ8K1mfNClIZAOlS60FCZZM9IJrJXzOtWrQvldU8h1MTxz79m552865EfMllZwU9e9Rp+6Z4XCeP5189PjouTaEHOiReF9lSijDk3SWhZ+PPCfMFptJTkpQ1aMRQrZwiNPZvntu2DzOaCYqX3uvY61n7+Y8fdiJwohu/C9mru3zsSCWet8HbuQoWSkptvR6/99HE3MmeDs11LbjAYDM8VjEA2GAynRf9EBqUUie4u4rkcxGIEzcuZfcmVRZE2b9PwC3Fk6XSMK9eUceXaevonMzy0f4w9vZNkJkNQil2Nq5iJV2AHCikEltDYoU/5xATxCZ9kVStZX6GUKghVTRSBfnr1ytfuupv3bvk+AHur2hlO1JDXhenvInFMQYRHx45sFAoBWCok68ZJhh4eesGEWhePI1SI1JpQCECQtC1K4zaPHBxn99pppBBHJ7LtHYgFYvNEMXzA0ZuMXA4VSs4LJmhbMLl/OizV3neyLGyTlGEwGJ7PGIFsMBhOi+aqJML3CXM5svEUuRACpWmsO7o5bD6q7Os/3s7jEyFqa19x+njl2nqOjM3RlBD4+SkOxquYjJchlEL54NjRBjulBTktGAot7MwsdSVJJtMBOduJ/L5KgTzePgEcbbtDo2TkO37l7nv5vYf/g3RJCV3lrXzlst/D1zaRFaMgjsMQLMm8PeOoAI8MHlKr4lBaS4ltWwQK0IqYCtBKkbddUl4Gl5AptxQtBPkg5NDoLI4l+dYvupiY9U86kT1RDF9ROA9PU3Lz7bR178Q5Jiv5TDjWEjPf3nc6teQGg8HwfMIIZIPBsCTzE0XZ3My+Ca848VzVUMr5HbX8ZGSGrLQRQImn+eHDPfSvytBSYtMZTtNllfPElKaqNL5o+piK2VEKQypBJpFizkkA0WRWWxLPD7CVonPyCNfsvo+hmhYeblxH9WQf5dpisKSGWTeJHeTxbbcogFnoNRaRwFWFoe7L997P7z/078yUlvJfb/pt9opWfOEcfe28d8OKJsWWCgCBo0LcIE8uWQqhT9zLkXXiUdaxlpR6GWbcJEEYHcKXkeDOWw55GUcJiUATsy0sS5DJB+ztT1NdGiPuWqRc67QmsguFs1776RP6xE+XYy0x8xPpU60lNxgMhucbRiAbDIbjmJ8o+n19fHdBFrIlBOevqGIiG+LFElhoLCnxNdyze4jd/VPEhvrZMNZNRyIkOOdKZvOSnBcSdy1CrSAIeswHNwAAIABJREFUEPk8OmkTLG9DDE4jBLhaYTmCXC4koX029u+hLjNFfibG3LIYOpFAZDLUzYzgVTZFc12l8Cy76G5YTDT5vWrfL3n/A98F4EhJEwdEC33UHvUn62PeLCTogJjyWTY5QMrL0N/QzkS8FM9x0BpsVYh2CxUljkC5DlY6yywOMS8PtkPoOBDqyGqRzaLiCTQwnfXJ+SEIqEi6JGPWGU1kT+YTP11O1N53qrXkBoPB8HzDCGSDwXAc8xPF7uZz2C7KqZUhsqQErTUP7B9lNh9gS0HMEvga5vJh5EwIfMoy0+yoXkFL/2OMzeTwpvNFp0LMknT+6FuMemXsqGnHa2iCmEtSKbQdI9CaUEiUr3i4eQO3rboCpTVZO86I4yDcCoSOfL9O4OE5C/zDS3Bl18O8/1ffZai+nqFYNV94yfvJET9qzThWHBcILQdPCNLxErzySrJIHAFeqLFUSEUuTUVmmslEGW8d2M2at7yan/7bNh6vWkHD7BjeqrVMe4qBqRx14RwJPw9VKXrTCksKXEeiNUxlPGwZx/MVdz45+BtLiThRcsmp1pIbDAbD8w0jkA2GFyBPFd01P1Hszyh0jYtMRjYIUfDTaqUQ+RwqDMhZMbSMvLijWYWXqiXhZZioaSJE4gchUgokEAQhT+RiXCrHuaSri5mL3sMv795DDylyjksuWYKNRefIIXJugiFpE0gLJSR24KMKNgotNFoWvMLoRRvq5rmi62Guu//bhLbFbde8kn6rlpxInHhyvBAhCCybmOtwyfh+7m7YCK5g0lNYwHSslCpvjhKpcSfGWFsdJ2tPstdvRKZSlFWWUKIUM5NpZBiA45DFIhWLylGmswGgCUJNoBQ/2HIEBb/RlIgTTaSfqpbcYDAYno8YgWwwvMA4leiu+Yniykf2YG+bjDyoRNnFMdvCUQHaz5N2EgQFwelYgpgjSafKcavKebK8k/zQLFJG8WxaCPJKcVfDuWzJz7ExNcFbP/Vhzp2Y4FB1K8NltQxWNPLw8k1INFnbxbdswkI2sSqMoYUKo+soXtHxIvnSg1v54P23INEcqGxlK2uYFOWnJo4XHHWmpoEnlzczNekR5DShtPEKScz9iUpq5iapHTqC3dLMRV/8FFvnNyXO+UgEr7q4jUvDUcab2/CdOD/YcoSqEpe5fEjWC0jnokl8VUnMpEQYDAbDswgjkA2GFxBKaW7dPsA9u4epKXGpSrhozQlFWdN3v8k63cqOmnbkshaktHjxqhoeOzjKkOUSRhV32BJsKfBDTaA0lZVlTM352JbAkYIwUMx6IXHHoqypHjyPrbk66uvW4pZn8aTDRKqCiXgpeWmhgLwdK4rjhWghEUpFzy1+BhBc0v0oH/7FzfQub2WgrI6bNr6DrJ08LXEMGltKLDSDmRDheYTIqKqkYM/IOgla5RAdrsfOPb0Mp6q56tLVXKVhcDpLU9Ki7rOfRPf1saKlhYqvfJnu0dmjfl4h6agrYXgmZ1IiDAaD4VmGEcgGwwuE+cnxPbuHGUvnmcn4lCcdVtSmUGh6J+YAiraLjswIuq+P99bm2d/VRfqqD9GyegXdo7Pcs72Pxtw0vmUz7qSwnBhNlQm8UJHxFG21KXbmp6hIOExOzBBogZY2Smv6JjMopfFCwT9f+Bbifp65eAolQKqoBGOkpAotrGMijguTYoCF9gOtosg3Ibn48Dauv/efkFrxyKbNHKlqIctStorF+clCK3QhsxitcZVPlZ9HZRWugJhwyNtxQIGGpGtTnp/l/LED/Pu6a9n1+DRKzCyaxoeHDjK5IBlC9fcf5+dVWvP3P9tjUiIMBoPhWYYRyAbDC4T50ofqEpfpjIdtwVTGJ531ERoe3j/Gf4wdKdouNi0r44K29QxO52mui3HVi1fxzV/1cs/uYSZyCjtWSml2lnI1y3QszuB0jiDUJGMWjx+ZJJ0NWFkqKJsbZSaWYogSVCDQyicQEo3Ec1wCy468xUKgpEJjgdaUZdNkYwkCy0YLgRMGRDUeAjv0yTtxQEepE0Jz4ZHH+aN7/xGpFd1Vy/h56sVkSB6Nf5sXx0WRrJgX3RqBVAqpFLYKqNIe8SDPcjL0kqBUzZFLVCO1RknJsslhfCSqrJxdHZuOi7LbOzjDmiWSIcQxfl6ltEmJMBgMhmchRiAbDC8Q5ksfKuMuFUmXqYyHH2rGZj3OXVZB9+gcVUkLcnmIxbht5wgPnPMqXBVixVxuv7WLw6Nz1JS4TM/msLRiOlVOVX6WElcibJumMpcUISIWY282YMiX2LEksdk0tY7PWLKSQFqFObCOCkGKm+0oTHqj4o7ZeJKo6iN6zLccpApJ+DkCaUclIAWBe0HfTj563z9xqLODfR0r+UH11czFShdPjnXBtawVEkncz+LZLhf07KAil6amLMYlb3o5Iplg+5xD9mc/Z/3gHh5MtLCzfhXx0Cdrx4gHObycx7mTh7HIEHo+QsfQsxkAlDoa27ZUMsRCTEqEwWAwPDsxAtlgeIEwX/oAsLwmRVXOZTzt8bsv6cC2BIeGZ5jac4icr9Cuy1yikprSGLVlUbzbjp4pQq2J2RJpWcw5cUIEg4kKbC9qknMPHibI5SAeJ9XYRl15ghK3lrW334eureE78QvIi8j3HMpjLRQUxHCEOs5/rFFCMucmcAIfraPp8abeJ/nY3d/AUSFDlXXsbVpJVqQWiOP5STHRdyGRBUtGys9xyeFtXN77BNg2Tu4g//GGj7D14e2E8SYeam9m/dQR/jC9g4G5AHnpZahf/ZL6ocO09e6j94prsFyH/JM7YXISDaiaRpqSq6KznUJWsUmJMBgMhmcfRiAbDC8Qlip9eNm6eq7d2MSuvimGJrNkrXIsG0INKtTEnMieIATMZD0yXhgNYwFE1GCnAT/U+GFIFykaYpJBWUJ6MsfgVA7hezy4+lriQR6kRSBtCl3SJ8wvLp508QMFnSvwnRgAG/t28Sd334SyJb3JRr7V+UZmROliW4UGRJRfHFoOru9RnZmkTIYE0qZR58CykCUl7JsK2fKLxymfnURICckkO6vbuPzQIV5W6VL5jlcwuf1ugskAedGFXPSlz/DI7XvZ2iVQ8TIkcO7wATrDzUDdWfrNGQwGg+HXjRHIBsOzkKfKKT5TrlxTTypmg4AL26tZ2xRNLX+4tYc5X6GkRaBByoIJouDXTWd98n6IFAKNRunidrmjaM1UrIQplSwWcdhaYwceWTcBAjrLXQ57krl8uEAAz0e0ycJ3CqL25Ne7YWAPH7/rRg6u6uTRzZt5IFzHTKLsmA150SqdwEcAIRrPiTFcUssUAec1lVLxti9jfebjhAcOMJCPPnshBCiFtGzkslbSL/8QVZetXrJQ44Ov28iWe39E36EB6ufGWbumBXfZsqf1ezIYDAbDbxYjkA2GZxmnklN8No45lwtY21TO3sEZnuybxrUEwrYJwyjRwbFgfHIOLx+Qznq4dtT+ZlmSbD4kOFG9s7QKk9sQH9CWE22uExI/FqfcyzFXeLVUCk1U+iHCEFcF5G3nKcXxusF9/NkdXycW+si0xxGrgTG3KnqfjgS3rUKEDpEabBWQcaOyE7RGSUEWl+1903z+e31sqNzI2/091OVn8CyH8UQ5CRTlG9bjZRSDTil7xvOsbowhj7FNWIkEl9zwV/gHDwEap6NjSb/xmfzO5m+SmlIWneE07rJlZ+XYBoPBYDg5RiAbDM8y5tMmKlPuWSuPWPKYB8fYWeYz5JZhCfA16DDayKaUxvY83rznPpypCfyKKn7UejGTqSo8XxGoBb7eY8VssbAjmjYHlhMdE0H/8DQZaRftGWq+8rkgkkMtjm7UOwFrhvbziTu+ynR1OWEWPn/5+5myKoviWKiQi448zmt33Mnta69kS9sFZJwEet52MT+h1pqc5TKVt9havpxNJfX8d+eVjJZUE0oLy7GRg3MkXZt7dg9x/96RE96oiHgcd93aM/rdLMWiG5owRPf2smGsm/eKHqq/8DnC0dETbvwzGAwGw9PHCGSD4VnGfNrE2SyPOO6YWuH39HLggR/SVB5DrLoWwdHgMwCpQlrJ0rbnV1irV9OTqOKe1EVkwnl/xQk8xMdkF2si20QoJXNYi60VzK8H7NDDt92TXsc5w138+e1fpb+1hXuvfhm751qYii8Wx2X5WX5rxx2sGutm5f2HmY2XsKN5LeG8cJ//LgQKGE9VIbXib6/9I/JOrHhZEgiVprkyTlkydso3KjqXw+vtpcsqZ2AuPCOLzMIbGjIZvMw0O6pXsP/AAVZ94Dr01BRWSwtVX73BiGSDwWB4BjAC2WB4ljGfNnE2yyOOPabKZMH3aEpI2rp30rLxVQzPCCw7qoUui9uUTkwyFNq0aU24bx/vHBqm9rdeww8ePERWS/KWu3QrXcFLLLQmpX0SQZ4pK07Ky5B2U4TzsW5FvaiJ+1kS+QzTyQrEgqnzQlaOHOQvbv8KiSBPycg03dlajsSbFtkqqjLTSK14snkNnWOHsVG8pOshhkprSafKmLXnxeRRsSq1QglJ3okj0Nh2FC0XKI0lwQujazzZjYrO5QgKpSDjH/s439KtPFnThli2DMuyTmqRWcpvvuiGJhFHxuOoIGS4ponOI49j19cT9vUR9PU9ZUqGwWAwGE4fI5ANhmcZS6VNPN3yiOOOqS3O09O09e/DaWnhfVevZuBn+3FtQcK1ScUsxktcrPWv5SEhaJQ+bWOHqZwcJodF/rhJ7+JmOoRAC0E8Hic1MMJ0aYyKzDRZO16IdxOLNuTl7Bh5y0VZFjEvyjleOJ3uGO3mL277CodWdVDVN8qnr/ko4ws9x4VzTiXLkFpz65or6Stv4Lr7v039zCgCRYmfJSvd6PyLEIXDaJASVUi90IDSGleCnpuDeGzJG5Uwm2XrH3+G/uk8jY5CTefY0dFKxdwUrt0CSfeEk+cT+c2vXFt/9IZGWtjr1uJMzrHqmpdh//1fLyofMRgMBsPZxwhkg+FZxjNRHrHUMc+pOh/Vfy12SwuVbozLzpnk0UMTZP2QnB/iBZp/GXaQKy4j7mWRyy5nZtAmZy1hgyiIyui/NVJFiRiTeRBOgoSXpTIzxXS89Ki4LkYTC7QolIdoTX5+M12B9rEjfPK2G8iVJdlyycV05+uPF8fFZUS2iel4Cb/suIhdjatomRxkrKQGz3aYr5KeT84QgBt6KGw8aWOJKOJu/rBSCHqPjFCZm8ZyXS6+fCOrG8uKE2PZ3MzXf7ydRxJr0aUOwveocCYIsjkIPETMLVg5lp48n8hvfuWa+uNvklY3QjLFtvd/ggZvhnUbz85mQIPBYDAcjxHIBsMzzJlEtj0T5RFLHdMq/HleQFFA941nuHV7P48dnsASApEsJ15SzpSnEXN+JORY4ENGcKwXWcnIZqFCRY3w6Ox6hN1Nq7FViK0CNILQOnaSS/E4Qiu0kLSN9fDJW79EiZfByzrsmFtOX7JxSXGMVlAsIImOM15azXhJNWiFG/h4lh3pchWJYyUkvrQpdSQp12Ymk0cXNhAmbMGGKkl/1wyXHNnGxpEDXPz2LyC8PBMf+ghhbw9djSt5/Pw3UfH/2HvzMLmu+sz/c87daunqfVW32pK6tcuWF3kBvIBtBgwJyWRfCSEZAhhCSMKQZRJgJpNJJgQbEwghwDC/LL9hmCQQJgHHGxjb2LJkW7b2reXeW91d3dXVVXXrLufMH1XdUkvdUmuzJXw+j59HdlXXrVtVdvut732/7ysimMmhHYfRutZKkkcpRJcDsOwlLTJL+c1HcqUFX2g66pM8tmeMT/7LPopBRKQ0W4cUv/+jW7DC4IxtfQaDwWA4d4xANhguIZcisu1SMSegAV6eLGJLiVMqoJVixvKIhcCLQ5S00MiFaRPV3OATQllUbQqCwdp23hk8yK1PPceuFRv5t/W3MZmqP+O5CK3pzg7wBw/ex66brqNtYIRP3vweJlJNi4pjK45QQqAWFd0AEiElAoGulo1YKsKJQ+4YeJ4f/YU30/aVz/JX9PD0qutoLOZoUiV0zsONoX3mOL3ZfqSQRIODxAP9xKNjDAS1BM39MDODDgLwfZyMS7PymappREYCORssaZE5k9/85C80e4dy7OzLMlMKmClFKK14bN8YOo659+G/QlctF2Zpz2AwGC4ORiAbDJeQSxHZdqkZyhaxpQCtUEohpUBVizxkHCG1QNkn4tksFRNXp65Cxeh5j6/AUjF+rHg0tYotmYDNQ/t4ZN0b0NaZf/V0To/ysW9/Ck+HDK3o5KHVb2DCXUwcVybYapEp9vzdVWIqvuj589aaJIoNGTjyP/4X+YEp7nJf5EhTN/XlPLKuDuX7SCtB28xxZDKJ3dWJ8DxkYyPhocN01BcRQYAKo4r41hopBO+6cx2JrdcwXIwXtcjMWTTWd3Yuy28+lC1SDCJmShGOLQCbIIzZdWySg1MBG1pazmlp71IV0RgMBsMPCkYgGwyXkEsR2XaxONlHeyAbzIuljoYkKdciSrlMl8ugNFKCUBGB5VQsDFVnhRXHuHFAyUlWvcRywXPE0iLG4jvrXscLK7cAmqKbRMYRagmR3Dk9wh9++1PU+rPMJGr4Dtdy3G1Z3FZRnWBry0JotdQrBQTRSc8nhSCTShIpzVfVJtI1IaJ1K1fPDnO1mGFHSzflIMJ1HLaN7GXDykZEMUE8Po7T20vj5z7L5PvuZW02y7XkeKGmARXHSDTXkuPat/4MVjLJ5iXe9+yv/8b8ot37Pn0fBza3n9Fv3tmYIlIapRVgVxYKBTiew/GO1aw9/Myyl/aupKsaBoPB8GphBLLBcAm5FJFtF4M5kRYODvI3G9/Cnt7rUUJgCcH1qxrnp5q2VUtYDll//AiqUOT7jWuROkZriCybWEgiaS20Vpz2ZJrQcplO1VWSIYQkXS6QTy5iOZga5mPf/hQ7br8Zr+Dz9yvexkSmeQlxfBKCiu1jiTuTcRmRSOBHlRZsT2vyQaUxsL0wTbqUB9thz+bXs6rBRU4GMD6JsBNo4ECcYGLdVtbLWjYqjayro/nLXyQaHOTDzc1s/+gfMTyep6PG5uYH/itWMrnklDYaHKyI4+rUVw0Nsam394xfmDZ01LJ1ZT2P7RsjCGMQUJd0Sbo2637j12hQP7tsD/KVeFXDYDAYXmmMQDYYLiGXIrLtYjAn0vo61/OCqCMVhZRtF9sW7Dg6yUd+aBN3belgaKpI2+wkrf/5fp5YdQP7oiIyncYbG+Z4pgnfSRLa3hLPsnCBL5R25TYhkUqRKhcouqn5+ztyo3z8W5+ioZQjmS/yb2tuY8JbRBzPN/Wd9BwLXBdzhSAKC6iPShSFTSmsLP6pWBNpDSg8HVP2kohEgnS5SHEiy0vjlSk2sUILeLz3FnY11OKlPKyHjnDD6qnKtLVaOR0ePkzvwH7Wt7QQjw+hJyZQtXVLTmntri6srq5zimqTUvD7P7oFDbzYP41tCZKOxY09TWxc1YKUrcv+7C/nqxoGg8FwuWAEssFwCbkUkW0XivZ9Yt/ncPcmHqGJsUQ9eiYCIhDg2pKh6SJvuXoFG5s8wiNT5FZ0sEM0MOOmsaVLvraFZFgisL1KbvBp2cJw2jT5pKW+XKruxI9oRXvuOH/wb/eTIKDoJvm7rh9ifDFxXHkFJx17kZrrasKGFODaFplSkWyyCX1yxJsAlCZCMpZsAK2pdVIopXCCAIIAHIdSsoaSk6Q16dFSl1p02jovePv7kU2NWC0tZ53SNj5w/zknT9iW5A///dXsOzbOwNFhVq5ZURXH5/bv0uV6VcNgMBguJ4xANhguMZcisu180b7PxK//RrXp7Tpydc3MlhR2GJNwKyI3X4roHy8QFovs+Mh/ZihXJqrvZrj3GpqUJFcMUEDey2CpCKRdXeI7H9EvaM+N8olv3cdzt25jur6OR7mB8dqW6gmfGuVW+Wcv9Ct5ynOtfWisOCYZlvCdBJFlozQkbEHguBVxPC+sTyRvpOMycTqDVjFTOsHm7DGmQ9BSIuIYHwsdhdiDL0PtOoS05qetG5u8eZHb8Gd/yuT77iWezDL1kY8y+N7fW3xKO5ZjbWkcu6vrvBrwRFCm/U/+kJbq9Fk8cD+cY2rF5XpVw2AwGC4njEA2GC5TLkXSQDQ4yMGpgJd6VlFbzFX8vUCkYbYcA2AJ+NaLIzy5e5AwuQm/NsEsFmo24upVTeSnZpgdm2Q03UhguyixRILEGak4hpvyWT724KdpLk6xfvc+Pn/nLzGeWEQcVwW4UBEN/ixXZQcZyzTTPT2CpSI6pkfZ2X0N9aUZCm6SofoV+E4C1xKUG5qRs0FlUqoVqtryJ9GsWtmCcB38csTs4DB3jO2mjzQvdWxAWxYBkrSOSRbz6JIPqRQSwYqUtWDRLvOhX0dPT89XQLcHM6dPabWm5kufY6pv93lHsp3qXz6fqunL8aqGwWAwXG4YgWwwXIZEseKPvr6bFwemsaUg5VpsW9N0TkkDiwlsu6uLsY7VxFFMKZWhpMCSEKsTZgXXlqQci75smbSXwVeCSAiCQLF3cJrWWg+VTBJZNonQp2QnUPKkCfLZpsnVBrum/CT/6ZHPUG5IUvI9/vjWDzCRaD5xjJPwQp96P4+tYtwoZCaZ4cbBl/iF576BRKPCiNDxeLFjPcdrWvBtDycOkFaStc1p9vlTzAaKuJqyIZUiGQe4xw7jbdlMKoqJghLdNQ63D+xi9KfvYnzFatL/8695YrqWl5rXVDKN8z7XNVr0FMeZOUmogl7gK968tYcbZvorU1oVI8KQ61tcVvftviBxez7+5cW4nK5qGAwGw+WI0GfaDP8BYtu2bXrHjh2v9mkYDGdFKc1/+aeXeGzfGJYQ84kFmYTNR9+xeVmi5kxRXvuOjfPfv7kHZTsMTZeIlaYcxtWmaE2dDmhtb6JvsohQGscvEEurUjFtWTgCIqXQGqw4BFHJOw5td17Y6kU9yVV0THM+y8e/fR99V/dyYMN6tpfWcTxdXTQ76XeSUDGW1txx6Ck2jxxgpK6NWS9Nz8TL3Pbyc9jpJORyoDWRsPiTN9/LSys2InWMrRR1NQlShRx+GDPq1aGkRChN++xxbtJTvOQ2I7tWYnkumw8/xy/uexCnOt0FCI8cRWnFkVQrw9M+NV/6HKv7duOs6EBrUCMj89NgYIGvWCnNvmPjHLz/r2gd6WNtrYWlFFH/y9irV9P4F5+Z/7lzuVIwF89nmvMMBoPhorDoL1wzQTYYLjP2j8ywa2C6smTmWGityZUCbEssO2ngTEtiG1e1cOPGFTxxYJxYg1SadLmIb3tIFO2z41CaQidbiLUgdhJVG4VEqBgRhWjbBSGIq6I4lhZ2HOGomJKbPMOZaRoLOT724P105MdJ7yzwtTVvWVQcoyslJDVBiSPN3Tx71bX4ToJk6HOgrYeBpi7e9/arKX3pi8iuLg73TzHU0IETh7gqRAO5kkWoJEUvhY1CSwvpWBRo4HUHv8cdra3M3v5GOtvqWN94PWroHuyuLnS5zOT77kVls9jd3Wx64H7WqZmKPaKlhXhwiJoP/Tp2dzdOz5p5oXryRFhKwTo1Q8vhZyqPGRoirq+v7BBWX+b5ZBKLanrG/NtkBLPBYDBcdIxANhguM+aa7LQWBFFcmSJriGJ9xqSBkyeRg9kisaouiakYSj5KCbYfmWAoW+SNm9q4Y0MrX3n8KC8em8S3HbQQKCxGvHqaClM0qimOZ5oRem65rWKdiKWFQFcX35hPmrBVRKY4Q8lNsHgesqK+NMPvPPZZ+jetJvNcng//2CeYTjRU7z/lapYQuHGIHQcoIQktGy/yCS0HNw7Z3b6OXV/4e9YGPno6x/iWbXjtrRRKMbqkEFqjlKIkbEIkybCEiCrpFL6X4vDPvY9333P1AlFp9fZWMqLffy/B9u3ITIaIE5PhubSKeHyc/KcfwO7unp8eL8bJlgjZ1Eg8mcVeuZJ4ZIRocJBDyZYLyiQ+tXTEVE0bDAbDxcEIZIPhMqOjPsmsHxHGMSqq6EYp4baV9UsmDcxNIncczVIMIopBTBDFNKVs4r17iX2f45kOvhUpPNeen1S+6/Y1/NdsEVnI4pZmQUqmrCT3HHuace3wja33oKpFIJGwEKrSVjcnjqVWKMBSCkvF1aW/xSeftX6ej3/rU/jNafZv3MDf9r5jaXFcva3opfGdBFOpyv22itFCENgeWsOYm6F3qh/Z2Un72Mt4zZNkhMOMsNBALCUN5QLHbY9YWFgoRBwDGqupcVExGQ0OorJZZCaDyuexN26cn842PnA//pNPMfNnn5xfyDvZS3zqNHfuMVHVrzz1kY8u8A8PHZo6p0ziU+0YPcXjF7y0ZzAYDIbTMQLZYLjcqOpLz7aItUZr8GzJj928csnL7vtHZthxNMtMKWCmFBGrmCDW7B6YJqM9/Jo6IqVpdxR2JjE/qUx7Nq5r0bKpB5WfJdy1i7Id8UzrBkZqW9GWjVQxXhiggVkvjRJy/hzVXNWzlBS8miWX8zJ+no996z5WTo9QKCR46pZNTCaaKncutQdRPZaS1vziX5EEntC4nk3oB7QVsqAhPHSIVdJic7Kz4kH2UkRhhCc05UQSJSVFN4mlYmypSXsO21Y3Lfq0srOTI2uuYajBZ0VCc9OnPjEvpEUiQeINr6f41e7TFuWWmuaebIk4Nf/4XDKJF7NjXL+yjp+5CEt7BoPBYFiIEcgGw2XGyFSJpkylRtgPYxKORSmIGMv5sHLxxwxlixSDiJlShGMLHBwoh0RaUHYSaKUI7ASDvuCqjJ6fVGo0QagYnw3wSgFOFBOkEozYrbSLMnFQYFq6zLqpqtWiElcmlK7EpVWX8ZRmSXFc4+f5j9/9S3bdcQP2UzH/6c0fJZ/IVO5c7pJw9dix5SCCInFrO9cM7IHOFXy/7XW07H2enuwAv/DM/+Hoil7GMq1Ea9fxN52vo6zAjjWh0igpqa/zuGsS0U1dAAAgAElEQVRzB5s6606byK5rz/D57/WzY8sPE5cDLM/lue1jvP/uuvkvJydPheeErvZ9/CefIurvX3SyPP8yTvEPn0sm8WK+8p39Od70W39Az+ABvG03GHuFwWAwXCSMQDYYLjM6G1PYUlLj2WQSDlpXROyZ/MedjSkipVFaobVFFMdEGrRWNHY0k9ARR6YDposhTeWIGs9GaOgfLzBTCimUI9CCZONKesb7GK5rJ79qHc1RgDcyxqDdRKY8S9n2sFVIYFXqpctScnKd9KmkywU+9q37aIpy7HG28t/e+iHy7jmK4/k6aY1Uiq2Du/mR3ft4Zu0tfPmuX0VZNqWW6+jIjfJjL3yLzaub2DA6yjffeC/FA7OkXAuPiofbD2Jev7aVe9+8DuC0ieyq5jR947M0ZhKI2uRpnuCFgrqFDa4Hc5PjgX7U+DgRYHd3L7tCermZxItWRKuYQ5/9Mq2Hn6FoPMgGg8Fw0TAC2WC4zDifprMNHbVsXVnPo3tHmfUjVDVrWGmYLkVcNfEyGZJMujX0jwvqUh4bVmQ4NlFkfUeGQhBTys1SyAXEtsNkuoHcxCwimcROZLBUTH0pz/GMhwBCy65YLZbwGwOkykV+95HPsCY7QM6r4ZverRTcmgXC2IrKICSWUsRSEkubiiCuZBUry55/DktFSK1ZlR1CWoIXZT3NCYsBH6ZrGhlP1DFyexs3Th7m1g1d9EUuWumqfUFiyYogbanzkFKwdyh32kR2V/80tiVoWsQTvKGjdtHEif+wWlYsDq1toCHzkd8m8YbXz0+Wz5YwsdxM4sXsGCIMaR3pMx5kg8FguMgYgWwwXGYsZ6q4WHbu7//oFqaLAduPTlYGuroiLWdLAbOhQngWQoOlNULAbDki1gopJRlX4428TCGSDGdaSAdFCok0IowpY5OIyzQUpyg5CcZrGiu+4DOQCop85PHP89xdNzO7u54Hrv4lil5qYcZxHFNXylPy0jgqRMdQtjxsFSF1jEZQkCd8zVpIUuVZrh7ex3CmmbhcphALposBjiXQfoAT+DzWuonnWhtRxwvEWlMox7hW5c1IezY3rql4j4eyReI4hmIRkgmEtLAtQTwvqhd6gvcO5XjiwDiuLUi6NmnPYsfRLG/q7aF9zgfc3b1AHF/MhIlFvzitbWPdHtd4kA0Gg+EiYwSywXAZcqap4pmyc998dQc7+rJEcbWwA/BjGE02UBQODdqntraO2XLE/uEZUo5AzcYIrYlLJUqJZopOYu6J0EEZJw7pzI4wVN8x30R3pra8RODz+w8+wLrxo4z3t/HXW3+GopM6zVKhpQTXxfMcCrFDjMBSClvFxNKi4KUqL0BrhFZIrdh6/BC900NIy0IqReFYP9S3QxxDFKOjiCIWLXFEa1s9UayYmA1IuBY1nsOt65vZtKLynq5IW6iBAabKJQIvRXp1N0nXYnVLDcfGCwum9+vaM/zO/3qekVwJWwoEgrqUQ9qzGS7GbDrFkwwXpxb61H8nFvviJO48/bkNBoPBcGEYgWwwXGGcqQRkKFskijVSVARVrDRKQ7KpAeGH5JXHxHiBamkefhxg+VMEToKpzAoUkrLtIrXCVhF25FN00ww0rkBqVa2VXnp6nAh9fuPpL7J6eoCxdDNfXvUT+E5iSb+xjmJWjR3l5UwbsetRlDZOOk0YxnNOC2wVkY58MnU1vPFd7+DosacZ0S5d0yMcdnuI4hgVBNT6eajG0CXsiv1jTVsGxy5yy9pm3nrNigWT+DXRNKJc5kiqUlIiRvP0tNfyez+ymcNjswtE6P6RGQazFXHsWBIhYKoQ4FiSjrok+ybLDJXSdE6W2dBRsXBcrFrok1n0i9Mpi38Gg8FguHCMQDYYrjAWXdaq+mSniwHVXhHC+IQoHcmV56o+EIAlBUprAiQJCTOhJvJsVPV+JQSB5RJIG6RF0UufcWoM4IVlPvrIZ3nxTTcw1tPJ37e9nbLjnTHGregkGEo10VqYJCubKLguWW1VpsvVAhKpFUUnQcKy2Z6Fvh/5DYK+Y5Rtm9ryLA3JVqbzeRwVElgOKR1RU1dZaFRKIxDUu5JoYADd2APJStPfEZ0mjmNWT7xM6CVJb1pHEGsOj82eJkKHskUcW1CfcpkuBqAhVprOxiSP7R3luWNTpzfhLZJ2YTAYDIYrAyOQDYZXmcX8xFKKJRe8OuqTlWi2mdK8F1Yi6KhL8q/ZUiVyDah0Gs/9rZqPZNOA0hrLEsRKMGBlaClOMuOl0VKi50WwAHHStPgM4tiNyvzuQ5/h6pEDhDs9PnPzL1O2q+JYKxCSE/K8gh2FhJbDrJuikMwQuR6xmn9mdPVxgVWptc6FsP3oJOtaMxzKNjEjbI5nWuiwPXqZpXNmEFIpJq7ezMu5iFiXmMwHoDUPP/w8j4YBW7/2GL/5R7+KlUwy0H8cHJdaS0FUxhGaCcGiJR1zySLdTR5NGQ8/iCmHijdtbOMfnh1Ysgnv1Fi3i4GpljYYDIZLjxHIBsOryFJ+4vfd1s30hz68YMFLux57h3L8j+8eJlsoUw5VZfHMtbln6woUmn1D0wilThK5Vea8w1V0tbpaSwtfOmSTtcRSVrKOBZwpneJU3Cjg/Tv/lkaZ52hTN39283sJbefE5FjIqkiu1l4LidQKhMZWisBySIsYYVsEQQxaz0tpjcaxJJYtacl4jOV89o8VmHFSVREN+UCzq6mHvvZePM+BsTxrVjRwVWstD740QruriIenwXV5gTr27DrCNbdsobunE8vZjy7GiGSSPBazpYggVCilFyxFnrYgJwS3bWjBseU5NeFdKKZa2mAwGF4ZjEA2GF5FlvIT76kNaT9pwSsYGOCv+xRPHBhnJFfCElCbdKhPOQSR5k2b23j2yCSlMMaJAwLLOU0Un8zcYFnEEU4czC/mnS2d4lScKOQjD3+OwW1r+J7bzT/VvJHIck63VQiJjGOSQQEJlN0kQghCy0IIKGAjwhiBRqgYLw4IpU1k2Vi2pKnGoz7tMDxdIl8Kq/XbAo2mGMRorWnJpKjvP4zyfQ6P1dN993W4jsRKeahqqgSpFGNuJS5v46oWbrptK88eHGXc1xRH86Q8h68900/f+GzFJlEVyUstyO0fmVl2E97F4GIv/hkMBoNhcZb+P6jBYLjkLOYnjrViZ8Hhid6b2V8UiK4uDlt17OzL4toCWwpc26IYRCQ9G8+VjEyXgKp9QsiF4lgsUuShdWVSKySh5RBZNvoMgnox7DjkI4/8JdcP7aHrhT7+b81tRJZ7Bs8xdOaOU3YSiDhCK4UCYmERC0mkQaoYW8Wky0VSoU8iDOjJSK5qTjGZD1Aa4qpzJFaV6TJao5TGLwXkQ4VwXVQYomeq4lVInC2bsTdtwuleSWdbZbIrpeDet27kp16/Gi8/zZrpIXqz/TSmrHmbhPZ9wsOH0b4/vyB3d28Da0vjiKA8P1nOzgaM532ys8FZM6svhPnFv/FxE+tmMBgMlxAzQTYYXkVOLX9QSjGZD3hw7zjuhjchem6tZN3ORsRak3RtBGI+57iUL2B5Hh31SfwgRlIp8TjV7wtUhesptwtBbDmcmCkvDzuO+A8vfZVydw3PsZn/dtcHK9PnM7TjuXHAmsl+8skaim6KWSsJ8xPrykJeLCQZVUJqRbpcAk8zXEiRFyUmZwOa0i6FckQQqarXWhNVTdeTZcVEuoXaoEhaaq7f0sXup4fYN5TDdST1SZcbe5rmxeuc93vf4VGsMKDGAnwf/DIKi6GxHG2f+OMFdgbgNIvDcpvwLgaL1VwbDAaD4eJjBLLB8Cpyqre1HFS21FY0JBBCorVmZ3+ONR11WEKQ9izqUg5ThTKRH1DM5bg5nuTRF+t5rn8GhEDP51WcxLxwXUK4LaKnl8JSEb/52F9hN0v2r1zPP2+4/azi2InKdOQn6M0O8FLnBkLLPl2SV5fyQmnTU8wiHBsdB0yk2ljbXqmn7qrzOHqgnxnhEEob1/IQVGLtglghbJcJy+Wqq+r49MNH6RufRSGgDCnH5j139iKlWOD9LvgBE14tUSDpTkhIeMhiTFswc5qdAVjU4rCcJryLxaVY/DMYDAbDQozFwmB4FZnztv7HH97EL966mrdu7aA54yG0RhcKCK1Q1UW161fWMTmZJ+1Kmj1Jz+wYV4dZCgWfx18YxNIRQTQXxXaS2j2DcJ3nDAkVJ2OpiA9954vc/PILFKZtvlFzB5FcxHN88qGVIhH6XD16kNsGn6fdzxHaznyqxokfrPw6kokEiVtuQngJZBhij43Q05Qk7dnoYpHO8X5WTg7SUJxG6hg/UpRCRRhrwmpO3f6xAodGcnh+kXRQIu1aDE+XeOilUWCh97urqYamhjTT6XrGOlYzOVXghu46Nm/tOc3OYCwOBoPB8NrATJANhleZk8sf9g7l+O7eUYLdeyqX+xMJ5Kq1rKixSP/L/4fwPURtHaM3vIFnpxo4IlqI05K4pBjy82ixjCW7s+QZL3meKuZd+/+RoZvW8O10hr+++efns4qXRCkailkSUcDrDj2N7Zf4iZ3fpP+OX2EyXb+IsUOTdCTKL0Pgo70EhAHXpQIKqxvZ/uLLhE4NEkXn5DC7U/UIBFJUvMwasITE0ooYgZIWQsVIrdDAgdEZ3k7nad7vVa0ZbEtwbf9u7ujfybo9LvLO+xe1Myx2m4leMxgMhh8sjEA2GC4jNnTUcl2jxfYjEpJ1EIXc2Gjx8PYj7EhuQmccyrEit2+UCElSRZSFRSytajveMqfFcyJ5mWJZKsUHH/8yG2eO8mDH3Xz5lp9lzjd8tueaTjWQDHzGaltYNz3ImqlBLMdCLSLm7TginZtlYMbCEQlkWXOtM8XV1/ZyNXDtlz/J8NAkbTPjfKf3FvaoDbhSEpx0Mcy2BY21CbKlWUIEyrKR1fS69e0V//Gp3m+AJIo7+neyIaUX2CdOtTOcanHQvs/kvR8k7uvDWr2aps9+xohkg8FguMIxFguD4RVGKc3eoRwPvTTC3qEc6kSzB1IKPvCOrXygtJcfG9zOB0p7uWvbal6YUjRYisZSDldCUdqVabGuZPIuKAWBs4veufuXKY5/deffc/uR7TzRsY1/arybmDN7jk9+HiUtCok03+19HSpWHFvRS7yYTxqILJvjIoEOQlocza+9/B0++O67sZJJ1NAQa6cGeMPgLtZMvEyNVChpYamQhK4cUQAddUmaMx6WFJQtl6J0KZQVNQmbN1/dDrB4+sTaNtY1uOdsnwiPHCX4/veJh4cJvv99wiNHl/W4K5WTkz0MBoPhBxUzQTYYXkGWKgY5OXPXSia55c8/Pn/J/uFDUyghcLdsRheKJPoGkEoTa4WOY4RlV3OGQ5Q8JeLtAhFa8c79/8jgTT38bUMz/7T2nsodyxHHC9Aca1rJ0aZuRq0USIltSaJ4rty6glQxGsj4s0wkkoys3sjjQYauoRzrOzuxr1qF0vB3PW9k96bXYU0HlKJKlrIEHEsiBIzmytQmbBpcKGGR9mwsKedrpJfKNRZ3nk9ChEZrDapahnKOiSBXEqaoxGAwvFYwAtlgeAWYixR75vAETxwYp6M+gZTytGriOU6+jD9vBxASpCTpz5JKpYiVwnc8tAYLhZbihDjWC4Xn+SC04tee+FveeOQp/qb5Z/n62rdUj72EADyjbUNQdBKM1LbSNnMcEcVIVbFURNKeq80jGZbRjkOQSJKtb+OrNVdRs30QSwxVGgY/fR97dh1h7/M5mjMJmhs0x3M+k4WAH922ktf1NjM24zM4NsNDDz9PU3G68l5u2cx4IVzQcHey91v7PtHRI9hdXeecEGF3dSGTSdT0NLKm5gd6cc8UlRgMhtcKRiAbDJeYuFTiL/55F89NRkwUQnLFkFk/pKM+STlS+GHMQLawZEzYgig4JSimm1ibG+bGvU8yna4nm6zjpY71zHppZpJzBRUXmMOrNT936J+548j3+crNP8232944f/uSnMW2EUuLQFp8f/WNBG6CWEBs2aArNg6pNUIrtNYo16OoBc2uBRpsqdm+d4g39TYw6tYSl8cp2BI/1qQTDlJK1rTWcE13AwC7pkZ5xPfRUkKphCqWkDiLNtxd6FQ0Hh/HamvFXrMGPTtLPD6OrFv4Wc59QRrKFulsvLRZyZeS+RSP6nv1g/xlwGAwvLYxAtlguIRo32f7b3+C7clNFNw0xVQtkdJMzAZMFUM8WxIrzZM7+7i7twErmZx/7Mmi6o2b2rhjQytf+e4RBhoamKpJ828yweaB3dQXpphO1eE7F+lSt9a888V/YPz6lXyp8Z083PL6+duX81hgUZHsRSFTdS3s7bmOjb3tzBZ8JvpHmHQzeIFPLCWB45FUEaWGJkRZM5LzK/Zqv4QThez/1F/SHhcYr7+FwHYQicr75TkWHXWVv9e+z4ov/wWbc3Xsbl+Pcjy8SLJt7eINdxc6FbW7urBWdleO0d19mmhcjq3mSsEUlRgMhtcKRiAbDJeQYGCAHb5Htj5DCRtPaGJLEMSaWFW8q/X+DEd2j7D92W9yy59/HJFILCqqrmpIMLb3EC0TI6Bh1knw8IY7kHFE0Ume/WSWg9b88jNf5a17v8NnW9/N4yteN3/7GR4Ec+6Kk2PftMaNA4QlQWnSQQHimLhUQo2MkGlvZ9yuVFPbOiZZ9umcGeWOYzsY2XA9X2+/DscSCKWI44ii7fFS2cXPZ6FRgNIVK4ms2kpERRz7Tz6FOnaMXxwZ5cjwbsZW9pK451q89gz7R2ZOm96ePBWVHR3ochnt+8sSf0pp9k+WGXzv79EezLB5a89pjzs5c1lUkzMWs9VcKZiiEoPB8FrACGSD4RKhlOYLh0Ieb9vCjHRRQoISOJYgjGNsS9CStGgdH2cqWcdwrkwwMMBhq47vv3CM7w36rGhIIdGoYoldxwrISGM7SUZqminZHoHlIGwFaCRUWuPOF6348ZGH2Hb8JT5593t5dsV11dvPMjnWVBv8FtyA0AoJSMclCkI2jB7mmsG9bL/qWsJDh5gZHiNX34kVR7TNHCcV+OSSGTqCGeTxl0l2XkcYA1pQtj2Uhp3Na3mhZR1Kaa7SeeLGRhKeQ7Ec8uyBMQ7+9/9N69BRVudmkBJ6ZkbY3vV29vSVUMeOnTa9ncsvbvizPyUaHCJ/331Mf+Sjy7JanPolRmrNdS/v4gPv2LrgSsCpmctCCBR6gR/aYDAYDJcXV5RAFkKsAD4OvA1oAcaBZ4Bf1lrPvIqnZjCcxv6RGZ4byLFy/VWURmaYLMWUI4VrSWxL4FiShtpkRYRFIe11Hl/YX2L7UzvIaYtpJ00UKVaMHatEa9W0MJOo47ibIZrLPRaiol+FRF1IeoLW/NzzXyfYWMf/f+eP82xmGeJ4bhlvkYU8K45AgBNH1CQstqxI8O6vfo1jqWbqizn6GzrxHY8IQWO5SCooIQAlJWOJetqGj9LcO0Fq9VXkyjFj0wpZ9mn2Z8B1mUq34LS305hOoJRicLLIN3YOECfX4/Wu5Yb6Nbz/9V30rd7CngMBjZnEadPbjU3eAu9x5kO/Tjw8smyrxYLJsFYEu/ew/Yhk++Nfn78SAKdnLmutkYhF/dAGg8FguDy4YgSyEGID8F0gD/wVMAS0ArcCKcAIZMNlg1KaZw5PMF0McO0EazvrEWOzTBUDahIWujrpLUWa8qq13NRoUbvtLez4v3upK+ZwEjXkVYLp2TKZUHE83cqUTFTqmedS3Ob9vnM3nOf0WCt+Zuc3eNveR/mTrg+wu23DwuMvxWLLeFpVkijQRJaDa0u2rmrid9++jgd2v4fnozSTyTqKbhKpFao6TRUItG0hHYcOO2L9qiauHjvE3rY2Am2htaY2LFFjg/ZnSda1MVGM8JVPOVAUgwgfjbZcZrXmu43rufVf/pXxrkHUhjedPr0dy3HVvkPsz5YZW3UDbeMD3KDVOS2gnTwZ1kW/0nxYvRJwsrhesGRJRRxvW7O4H3opflCW/AwGg+FK4YoQyKLyf7e/BQaBO7TWs6/yKRkMS6KU5rMPHeDRPWOM58tk8z5NNR69bWmGp2zesrWD1ozH8RkfKQXbVjexqbOOR/aMoi2bopfCjzQpJyaP4Ei6jfBMFdKLCtXl1klrfnjqcVa6E3zint/iUOuaE48/D4SG0LJx4oimUo7VPSs4lvV5+OA0e9bfSKrsM5YLSYlKKUjGEeR0PSNxRDIsc/XxQ6xvSRHt2MHPs4t+d5YDP/krfHv3KE25WWaVoJyqpzbt8s7b1uC5Fs/1ZfnXF0p4lkDpShZx0U2wq6WX60aOInpuXTi91ZrUlz7HXwQZXuy8FW27WL0buKlf8v5P34caGlrWAtqCyXAyAdUrASvqvAXieqnM5eUK3B+kJT+DwWC4UrgiBDJwJ3AD8MNa61khRBKItNbhq3xeBsNp7B3O8a1dI/hBhI4iykIwMlXElnDbxnYKfsQ/7D8+L3Zm/YhNnXV0pC3GR7P4Th3CleC6aK0rOcEnLb4ti2WJY/jx5/+F1vo831n3eg4l15y3MJ5/2mpUW2t+goZynuP5Zo6rmL977AABEj0xRSgTCDQkbRpcgevnuG7sILcf3c7aZETN7/wO+cFB3BUr6O3fy/VtEeP/+iKPuJ2UbA/hOHiliMf2jfHLd/TQkvEA0FIiLAsVx5XXHwSsa3DZtraNnf25+entdY0W6vhxdvdcQ0Mxh9O9EtFQz87+HAeyAZuWuYB22mS4eiXgpnf85Gni+uTM5XPlB23Jz2AwGK4ErhSBXG0ooCCEeBq4GVBCiO8CH9Ra73n1Ts1gWHgJ/LljWWb9kLm1NQFoIXFVzBsyAQ/syi/qiY3Gxk4048UxcblMJG2E1hVrxUVEoPiR3Q/y1v3f4RP3/CaDyc4LFsdohaVjQstlpK6VIdFOVNRAzKwfAxphpUAIQq2RYYyyXJK24PaDT9J7vA9aWvBuuJ5Sb++81UEKeF3/8zzb20ZjqUA+3UEx1jx7ZJKjY7O01Hp4tiSMNSTToBU1tsWt7/lJmq9by72ut2B6u7bB4QtPbyGnbZxULfUN9QjLRhGd0+LchU6Gl4tZ8jMYDIZXnitFIK+r/vm/qfiQPwl0An8APC6EuEZrPXTqg4QQ7wHeA9Dd3f0Knarhtcapl8CzswFRrCtDXAGW1kQCspMzPPmlxwlbNyI29YCwFoid2K0lJWJS5RIiitCux6hdUxVGlWSIi8Xbck+SWCn5w+7/yEhd24WLYzRCaRJRGS0lsbSJq6JeaFX5CSHRomLD0EISK0V/tkRPayur8mOQTILvE49P0PjA/fOJHjunfQ51bcEOyyQdyXEFri0IIsgWArKFANcWWFKQ8iw82+bW9c1cc/N6hBQImJ/ezn1WT6y+kdx0kYJlkcv6dDelFl2cO5v390Imw8vFLPkZDAbDK8+VIpBrqn8+r7X+ibkbhRA7gCeA3wJ+89QHaa2/AHwBYNu2bReqAAyGRTn1ErhnSyZny2gNUloowBGQ9H2sVBoRBpVmt5qaebHTUZ/kH54ZZzJZj5XUUC4TK42SFlLFXExx/EO7H+L6qT38n5vfwYh3McQxoBSiunroonESDrlAI7TG0golZDUNToKOcVWMBtoyDuHoGMdqWunNDoBjAxrtevx1n2Jn3xFirSmv3sZMIcCu9SBXJoo1SmsSVuW47XVJwljzlq0d3NzTvOQkd+6zWtGYJFSaqULARL6MLSW3bWhZsDh3uXh/L8aSn8FgMBjOjStFIJeqf/7dyTdqrZ8UQhwD7njFz8hgqHLqJfBMwqHGtSiGMZYQWFJSn7JJ+oKrR/dT6E6yR1moGR8/jPBsi88/fJDBbJHGtMOMHxN5HuUwxgvLRFqDtJbtK14KieItfd/l7gPf4xP3/BZTXv1FmRy7UYDWENouZcejKeWQrEmQmyhRaRCRlfOv/BMIQYREAg0Ji0KsON6xmt7cMM41W7G7uti9fQ87juTmrShKKfJ+zGygiJQGrbFkJSoviDUpz6YUxnQ1ps44zT3xWUlWtaRpri5L3nNtB++8dc0C4Xu5eH9fKSuHwWAwGE5wpQjk4eqfo4vcN0Yl7s1geFU49RI4QEd9kqaMx8uTRWwpSLk2N9x2DTe9+wZe39XFvokyX370IM8eyxOqE8dKexbtdQkmciWiQNEW5ulPNF2wOAb4d1NPIVd5/JcVH2YqcW7iWKgYLS1EHKMF1fxjSTLw2TB2iJKb5OWGTpIqws3m8GcTpFN1lMqKaK5dT4hKmYmQKFEtwPM8pOvQkZS4t9xC4/2fYuojH+VgMUm48uZ5K4qUkqaMy5s2tbF3cIbDY3mmCgHlSNGQdkl7FuVQndV2cOpnlfZs6pIuN/U0nyY4Lyfv7yth5TAYDAbDCa4UgfwsFS/xYsGkXVQykQ2GV4XFLoHf1NvMe+9ay8HR/IKpH1Qmk9sPjrHv4BCRlcCisoSnNBTKMYOTRUATC8mIW3eiSvkCePP+x3l939N88c3vZDLReG6TY62xtSLSAolGKkUsKmbilVODSCAVlOgoTvH2sed5ZsU1DIcW7a4mn5/FldBYnGayczU+FtPFEKUhVtA3UeQdt13Nje++AXflSqLBQcLBQeJVNzKrBDPTBTINGQAsIbmlt4V33dbD3uEcX3n8CAMTRTRwbLzINd31rGvPnPGlnM2uMNesZ3d1Ge+vwWAwvIa5UgTyN4AHgF8RQnxFax0DCCHeRmVZ78uv5skZXtssdQl8nqoWVVrz+UcOsbMvy1SuwLT0qGQ8yMrPVEVrOdJYKkYIQWi7F3RuNhFvmtzOrUee5o//3YfwncS52yoESBVX/MNUJsDJ0Kctf5zIcsgmPSSK65im8/ggky3X0FYaJ7FpNY2TI2RDyV2M84+JDfizATUJG6U15VCRSTjceU0nXld95b3s7ORvNr6FXaIO30lwcDokVcrRmklwY0/T/PsqheDuzR08+OIwxyYKOJbk2HiBzz9y6FPqVl0AACAASURBVIwe4TPZFbTvL2jWW//p+4z312AwGF6jXBECWWs9LoT4AyrpFY8KIb4GrAA+BPQB972a52f4weBC2spOvgSulGbvUGXCOZgt4dgCW0quakhwdHiK5oYawtBjNB8yv3x3imitJEBc+PLcHcefxWkTPHDPe/Ct8xDHgB1HaCFpns3SWJrGtz0KboobB3fTMjOBg6IjJbn+g+/m25MD6FQa6cfoMMLdshk5OUv97Xewct8Eo7ly9aiCltoEac9mZLrElqpAPpAN2NN7PS0ypi2ZJB/ETMwG/NQtV3HP1hUA84tzhXLE8RmfxrRHV2Nlqrscj/Cpn9XcZ95WmKR1cBCnWjWthoaW7f09efJ8toIRg8FgMFz+XBECGUBr/edCiEngw1SEch74GvC7WuupV/XkDFc8FyuxYD5G7MA4I7kSthTUp1y6G2ye33UUKwyYGZ9iyssstE4sKlwvzHd8++Gn2da3k7+8613MWBmEVvMV18tGxWwcPcRkupH2/HEEMJWsJ5eq5dENd+CGPh254/y07ifx+lvo/JfvVFI3kklEMoEWEiuRYGVbPe9qqWMwW8K1BUnXJu1ZZGfDBZaFoWwRJQSyphJck0la+JHCdSRSCvYO5SqLc0mLuOQjgVwpYLbskUk45+QRPvUzl1qzeeNb+MV9D+JUq6bFMry/p06eGx+434hkg8FguMK5YgQygNb6K8BXXuXTMPyAoZTmW7uGeXTvGM01Lo1JF62XN408deqslGZnXxbXFtiyspQ2kS+T1DF2GFByEhSETayWmuReeN6xS8gdpedYO76fT979PmJpI5RCn8+in9bUlAtk0w1oy6LkJplJ1+IkEsRhiWyilvFUA6O6mxeeGua9f/aH3PjNF3k+G6MK4Wm2hFvXt7DjaJZSGFMOFTd019FTPI72PUQicVbf71C2SBzHRHsOYIcKkW6BRJLpQoAfRASRpqMuuayXtlhKxe7e69n+5jeQaG2ma7LMhg7vrF+QosHBijiuTp6jwUGcZbbxGQwGg+Hy5IoSyAbDxWZuivjo3jEm8mVmiiF1KYdVLen5aeSGjtpFrReLTZ2balxipUk4NkGkUEqjgP58SMpNYYUBZS9R2VATYpF0igtPq7hp8Hm8tpCvvu7fEwu7kkAhzmPRT2uwLA61riFyXI41tWEV8sRIUsUZinYCJwoQQuAGPjsOjXFoayf3/sRNfGvXME8fOE5N7HPbmhNfMN64qY20ZyMEbOvK0PYnf8jU4CB9q7eQ/5X3s6KllutXNbCzb2pR329nYwoRhijfJ+261ARFJiyXkekSsppK8djeUTZ11p1V2A5li0RKMVuO8IOYhCsZm/H5n/tj0sd8LDG0rKsIdlcXsqODqK8Pa/Vq7K7FdokNBoPBcCVhBLLhNc3cFLGpxiVXDLAtmC6G5EuV6WdHXXJJ68ViE8iByRJCgOdUBOmcc0IDYTKN7XqIIEafmkwxF4WmVSU3+DyQKG7qe54NI4f58sqfRSGRKkad5/EQAqFiMuUCDTOjjPlNbBnez+6OddhxRLG2tSrwNQnbInQcvvncIHsGcwxli4R+GbTmu3vHeOu2bghDXsjGKCGwhCA3Ms5PDg7yd+vuZpfdiHj8CHYywfWrGvntH9rIyHTpNN/vho5atq1t45nRMVQY4liStGfTVpcg6dmkXYudfVPLyiruaEgymQ8oBSWU1pWFyVjR1ZiiLuWdU+7xXGviRUjjOyvG72wwGAyXHiOQDa8pTrVEDEwWiLWmIeFSn3KZLgaEsWZiNuDOTW0gWLIsYrGcXNcRtNUlODpWQABCx5WuDKUoxQ6+iqtWh5OU1Lz/+PztFR4Bd6jnqSXLF9/w8wAXJo6r52Oh8STIZBI3DNg4ehAv9Pn+6m2E0kZ7CRpTNrWtK3hhcIa+8QJRNddZIqgJfcpC8siTB/AkdMgAd8tmFIKnxopMb3wbL1n1tIsAr6kGLSQ7+7LctaWdN2/pWHRx8t63buTODc0MHB1mRCR59OAErbUnbBXL9SFrpSmUQ8rRQruL4Nxyj6PBQeLhEeyulcTDI5fUYmH8zgaDwfDKYASy4TXDYpaIVc1p5iTkVc1pGn2XyXzAr9zRwz1bV/DIntElyyI6G1NIBPlSpbDCsyWWkLzr9h6ODU/xlw8dZLYc4QlFUTgoAGEtPKkFy3nivB0Wm/v34tWWeGjN7QBYcVRNwjhf9NxfEISooIhMZmibmeBoUzdOHODEIaEQFELJsckiQaSw5s5fa5SQBNJCC0HeTRKh0MUZVLFEf6nizc61rKcUROhMglVSIhALrC1LTe83r2ll85pW9g7lePxw9ryyip89OkkUa6Sovs7q650qlqlNucs+lt3VhWxrJTx0CHvt2ktqsTB+Z4PBYHhlMALZ8JphMUtE33iBVS01HBsvzHte79zcNh8pFkSKWT/CswMyCQdgXjSta88gJRwczaN0RduurrNZl9a0ffFPedC7hl21KykIh/hUYXyRSOKzfvAgXbkx/vmqtwJgRyGRdeH/aTtxCEIwWVNP6NtcUx4H22JP5yba8xO0l3IUHY+pmkbiooOya047RmA5aCkr5SLAYLqFFmEzXSxiyUrU2/B0ialiSLMfkfbs+fd3OVXPZyv+OBPj+TJKgxRgVT3lsYZ8KWY87y/7WLpcJtz1Imp6Gl0socvlSzbVtbu6sLq65ifIxu9sMBgMlwYjkA2vGZaqDn792hZ+6parTiv5+NzDByuJC0HEwZEyKc9aUFixf2QGpTRr2zOUgwir/2XKuZgdv/V1eob7+ZmVaV5ONTGZqL8kr8ci5lb9IlZdyD+vrIhjJwoILecCj1yxesTVaujm4jS/tP8h1js+32tfhaI66I4ihOMR2C4BlS8AQsWgT5hxlbSwpaC1LoGOY7JllzBXJoo1zRmPllqPYhAzkS9zfManLumybU0j6xtd/u2J/cRxfMaq5zMVf5yNltoEsup2UdVvOBZw5+Y2rl/duOxjlXfsRM3MIDMZ1MwM5R07Sd5153m982dDJBI0PnC/8SAbDAbDJcYIZMNrhqUixLqaUqdl3c7l7TbWuDRlXPKl8LTCiqcPjTOeL5P2bGqJiIIys4k6ntN19DQ2smZgH1dlNpBN1sOSsW7nz8bhgygZ8/2O6wFwwoDQvlBxDHM+D4GksThFLpHh8Ad/j8YVKdYcPYZ8chjtOAylmplJ1hBIByEEjoQoFpV9QzQJKUmnHDqb0mQSlV81zmSRtR0ZjozOsqIhgRCS7qYUtpTcc20HN/U0s77RZfpDH6Z2KkD/P/bePL6uqz7Xf9ba05k0S9bsWZ4TEsdJgCRAwhBCaX/QQgdKSydKoaUtFFp6W0rnmQYolHu5tND2/nrbpqU0zJAwZCSJncRJPMSxLVuWrHk80z57WOv+cSRZkiVbY2wn6/l8YlvSOfvsvc9x/O53v+v9bn0tOrMFYdkLRh6mBn/srPOIursRgQdzhON8WeYbNtfxH490UShF0+nvlGfzA9e0sqd98Rc13r7rkFVVqPFxZFUV3r7rVnT2L4ZIJEyswmAwGNYYI5ANLxqWcjt+ptusddldLIUxDz83SHt9iu8e7ufLj/eQ9SNGckG5sS3diNSKb7VcQ+fWO3jT9/5/3vz9L/Dcy9/BWLJy2e0Uc6kkz+bhU6T8AvdueTkAXuhTsr1V2T5aY+sYqWJCy2bMTXH3o6d5YEMLe1vb2TP2ffbXb2U0VYWlFVV+lqKXRlgODUGeMFJEtssrrt3Iwe4cadci50cUgwgpBD9+4wa+d3Tg3PugNS9vtPmp61uwkknC48eJu7vZ1tDAVUOdHBptQnveBd+vCy1eW2gIzC+9uoM7XtLCg8cGKUUxnm1x07aGRQ0ZmYmsqqLhi18g/+93kf7RtyKrlvZ8g8FgMFx+GIFseNGwlNvxU26zUoqu4QIDEz5RrLnv6ACPnhxGK01ULOJoQSgtFBKkjWNBVibYf2aC03Uv49oml4bsEGOpKlZjCAhorouPYFcpvlV/AwCJoIjvrPxWu1QxnooIhIWrI2IhyLkpLBWTCn3ios8Dx4r85ttvp7Zrgq+cKlA70kcy9OmtbmHU89B19USlCCElh3pzTBQD+saL02sR057Nd4/08+7XbOPY7iw9/eNk/v7v2NT5DGP3loXtzJztzzTb9O69g4F0Ha2NVee9X1PO8OnDJ6kcDdg2z+K1hbLMx3Zn+eXXbuPVe5qWHM+YiRofZ/BNP4waH6fwL/+Xxm/fY0SywWAwXOEYgWx4UTD3FvurdzUhpUD7PuE8ec4pt/n+o4MMTopj15YkXUkxUESRwtGQ1BFSaUqWO92G4DkWQaixgyLf23wjJcdjNQaAAHT0n2RMJzja3AEIkqUCRXdxk+MuhBf6WEKj3RRCgK+cspyPIySas6la9KhPrOFTdx/j5zjFQ1tfR2rwDFJatGT7cVsa2dexjsdPjdBcnUBKScKxeLY3S3N1guq0R9q12H9yhG881YtrSxqDCdZ1PoPV0MDR0YCJ+4+wYddmtn/8TuKTnWTvvJPmP/swbVOu8BxxPO0MxzF662u5aqiTn23zZi1eWyh7PpVlvtgo6YtR2n+gHK9Ip1Hj4+dlkOeLdyxVhBsMBoPh+cUIZMMLnoVusb/7lvWM/dr7FuyUfdWuRnrHigznS0ipSLoWIBBCgYqJNUSxInbKYpI4glChAh8da7RSFLxUWRprvaIpEjVM0FE8TVZ7PNF8FQBpP0/eu3id2QXRGicOsVSMsh0iAaipZgcgVgS2Wz4+ykNBnqtt58vDRa5tTrJ/sJm4VMLyPG7Z1cK2tmqe6RlDTg5CCaKyME26NhUJBx1H9I/m+fvvHieTdJBas2vn7eiJLM+0bEI8l8c6eZjrNtXyzk0O8dneBSvNpp3hpAV+BNs3caipiYEf/AXqXI8jPeWBJUGkkDCrjm+xVXCL4UIZ5IU+exebzmcwGAyGS4sRyIYXPAvdYj9UGdI0p1PW2ryFw2fH+fz3TtA9Up6wFoQKpRVoC43G0horDggsh7yVmha+MRIlBTJWVBazAGgEQitWGq/Y5p8mkyjxeGoXABXFLNnE+bVqS0WicVREwU2BlOUR2IBQGltaKMQMcVz+zdKKo3Ub+cm9G7jhvv/m7Eielto0N976Jp4dCWYthPQciQA8W4KKGT38HAW7iubsALW7tqGF5PENL0GFIW0NFdOL8fafHOHWrVtoukClWc9IgTiOiQ49i/Z9RCKB3rCVnlzMd2aIUqFhKFci55cX40kBm9dl2NZUMWt7y3V6ZVUVjd++h9L+A9NieYrFVNUZDAaD4fLDCGTDFctiBc1Ct9j73UpaZwgw2drK391zjAeeHaR3rIgtBVUph/qMQ/9EiVypvMgs7drsmRjgEdE4abNOvqZWODrGjkOkjgksG6nVZCfxcsWxZsNwN32imp5kC7GWVBbHmUhevOd3cVsX5fzy5Mjoqf3UQuDHGiGsc/s+ef4iJwGVLt1nBtnXdZjtDQ3EXadRPT3s2Lxl1kJIoWFLY4Yg0gzkcoxrm6SOSBey6KKPSKcpxRotLHKhxs/7JFyLWCu68xF86A/pOtHD+i2t1LjerLPYWptChCHK95Gui/J9RBgSxmqWKM0WA3J+SFttCsuSeLYkCBXH+rLTInWlTq+sqpq32u1i8Q6DwWAwXJ4YgWy4IlmKoFmo3q21sWpWp+yR4RIHOkdwbYktBY4tGMkH1BbHqIk0O9UEO2+/mXW1GR48lCY4PjZ7p6RFoCWeCImlRcvoIHk3w0SyYlnxigZG2a06OWmvo6u6HaliqooTkwv+VgeBRk0L/Dk/1EyOxZ76uvwApWPGChGPDsdcV11N1N+PvX59Occ9z0LIbU0VHOsrL8gb+ceH+JLTjkgkEMkEWmtcSzJaCDjRn53W6K4lefjZQU4PlwWmdfIE120anfX+7miuZF9HI4/09aPCEJlKcWNHI7YlZonSUqTQCCxL0lBRjtAMRv4skbpWTu+Cn71VincYDAaDYW0wAtlwRbIUQXOhejchxXSutWdklFhrPEcSKUXJh1hpBkUSXEFuYoSnTwxw9vAAw/2jaGuyVk2IcyOjhcC3HBIaztY0TQcU0GrJNW81uVGitEVvdTNWHFFZnGA0XbOi8zYLrREqxgUCac3evylDmbKInhmzEGhqXTh+8DjHAgerpYPsz/8q7cMldjR7073EM9+Hqa/jne+n7+6DPDESo/IhEsHOlioePTlMFMfTpzLWmiNns7TWJsuuq1Lcf3SQtGdz49b66bsFv/z6ndy2o54zJ8/SvrmFnRsbOHx2nCBUDE6U3WjXEkgxGfOAaZHakrIIjx/HbmtbM6d3JZP+DAaDwXDpMALZcEWyFEGz2Hq31toUEhjOlohUWRwDxELixSHPVLZDv4+LPieIgVnWq9ZEwmIkXU3aL1CYWkQ32aW8GFwC6sZGGHGqOSY340Qh1cUJhjN1SzlFF0cIYmlRERexAyg6SfTk4jq0RiqFkhJPRSghCShPyGuKC2yoytA3GHLXppsZ0Q7i4S7s5AB726v4xQ4Ht7193ilvVjLJe99646z34sxwnpODOZKuhR/GJByLgXGfYHKKntaaruECQ9kSdz/ezX1HB2bdLdi9eR27N68DyncWvnOon4liSL4UAeVquU0N6bJojsojpK9bX8W6P/89RifjNS0f+sM1cXqlFLz7lvUcqgzLkZ55quoMBoPBcPlhBLLheWG1q65m3roGyPohuWJUXlCn9Hnbns/VnMuO5ko2NmT4zuH+8ujhqX2XFoFllePGYYgVlSjKGX919MwFeGW3NZY2E6nKyW8LENaijqueMa6TxziSaqPXq8MLS1QWJhisrF/U85eMkGRlgpuKp2GswKNNuwhV2SVOxiVsL01ee+WLBaVJRj4tUQ6RaCd0E5yNLJplgFuXQWvNI/c/xd7PfZMdNe55rSBTzPdeWFKQ9mwyCQetNZ5TPl9aa3KliLFCgCUF6yoTpD17wbsFR3snePzUCDtaKsiXYopBRBBp3n/HTixLTovyLYUBxmcs0Nwaj6/I6Z2qd5u7SE/7PmO/9j6aurtpnaeqzmAwGAyXJ0YgG9actai6mrp1/diJYQayPoVSTMqzuOuRLjoHc8vatpSCl3XU88TpUUZzAZFSSFHOs8a6/JdFaIVQqqx5VVx2XGdWuAm5ospjO1uiL11Dj9dEMihSWZygv3Ld8jd4UTRKCLplhnfbXfzkj1/Fg5+5CzUxzksSJQ7c9Ha+d2yYgl8izPs4QjOsbNzxIht2bqR/KItbXwHSgnweFYYM1LfRcfpxwhMnEZ57Xsf0XOaLIdy0rQHQHOgcZawYEMWa+gqPTKK84HGhuwXn7ixIMglJJuEwmPXpm/B57Z7m6cdr35seRmK1teG2t/OeLd6ihsjMRY2P03/ba6Zr3mYOCom6u8uvsUBVncFgMBguT4xANqw5q7kAaqYT/apdjWyoT/O5+07SXpuiIumgNcva9tR2z44WiZXCliClJIjUdJrCkpKkCshLi9iyywvY1NKzxfNRzxg6FzHhVdJnNZEu5akqTHC2umnF274wAi0lZyoa+WT7BnYdHuXGt/8ILWEWuW4dT37jOTamBbKuitLhs/Rpl1viQV75g7ejXZePfuUIWpSr3Eh4SMdh3VA3srmZ7J13Ep7tpXPTHrI//x7aFogXzBeB2dZUwdGzE2QSDgNZn8dPjtBamyzv7wXiD4tdFCcSiVkLNEUigYBlDQ0p7T+AGhsD10WNjc0aFDJzKuB8VXUGg8FguDwxAtmw5qzWAqj5nOi6jEvGs6lIupPbZknb1r5PcOYMn3ku5EDXGPlSRN6PKUWKhGNhS4HSmlhBEGsCL4MVBGilQU6+4AoGgABUkOd661mOp1t4TjZQVZygopilu6ZlRdudhWB2FERM/4JQMbaKGBvy+XY24FDPOGnPpjblE3SdISqMIRIJvF07SY4W6HjFzezevA6l9HnO7423XM2+n7sOEQSMfPC3+Odtr+GgXYu47wR2MrHwnYPsBJuOHmDHvuugonLW+ywRpBM2w9kALbhg/GEpi+JEIjGvm6t9f5ZwvhjOtg4IAvB9kLL89YzXmCvEDQaDwXD5YwSyYc1Zraqr+ZzoM8PFyeaDpW87LhZ59AN/wH7f41tNV1PKVBEoKPebgR/GaH1uCZ5GIwWUbHfOIr2VYRVCnkpsotdqoDY/SsbP0VW72k6jxtaKSEz1Np8TqFKXF+OVtMYSmoRrUZN2OdM/hooVuC7a91F+CSuRoLWxfOFxocWP2vfp3LSHg3YtNZYqZ5SFnNfdnxtRGP6XL573Po/kAn70pRtwHXnB+MNiF2QueJZ8n5Ff/fUFpyvOR3jsOXBdhOuig4Dw2HPY7e3TP19IiBsMBoPh8sUIZMOas1pVV/M50a4jaKxKMDQRLGnbSmk+efdBHkvu4mxNDQVpgx8D4FjlcrapaLGYamrT5ZhFiFryOZiPDaKPYtFiJFHDmOXQkB0i7ec4Vb9hVbY/i1iVhb48Pw4SS4tEVEJZFkJIEo6F0ApXQK32GdY2pFI4ymLfltnndqHFjyKRIPvz70HcdwK3LgPSojzF+nx3v7T/QFkcp9Oo8XFOP36EWKfOu+PgOpLX7mm+6KEuZkHmQszNDIcnTiA874Lur7fvOmR1dfkYqqtnjZo2GAwGw5WJEciGNWelrt4U8znRlpD8zCu2IIVY0raP9k7wxEiM41jnyd0wPucOS2BqUHTZVY5Y0Sq8SRxCtopuepP1DLCO5vE+kqUCJxs2rXjb8yIt4jnfsqVAKY2QkkRFhlykqU466Cik91gXJQVvzR2l8m0/wUC6bskVZW2NVdjJxHRGeSF3f6r5YcpB3rB3J9Z3uy7JcI1ZmeGWZib+5mOo3t4LuskXGjVtMBgMhisTI5ANzwsrcfWmWMiJ3tVSNb19KLvDh3vGL1gp1zNSQAkB7RuRwznsICaanoxx7rGx0lgqIpb2rGEZK8UtBTzs7qEgE7SP9JAMihxr3LK8jc2Me8zJQ4vJhYR6agLHjJ87EqRjUZlw+KG9bZwayvHoiSGe7c6BnSEVlXgwquJ9TRle0rF+ybu12DsHcwVmY0Ul153OXZLhGjMzw7oUMPbB31xUA8VCo6YNBoPBcGViBLLhimEhJxqYFsTNNUm+c6ifx09duFJuyo22XUmoOM9dnUm8Ci0VZTTbxRniCE547Wgh2Tx4Gicq8WxTx8WfPg9SRdMREIlCCUksLaaE/KxYxRzxXIo01Z7FbbsbeccrNnO4Z5yjp4epKQ7j+gV0pHi4fht7Rm3eME+39EX3bQl3DuYKzNW447BcpjLD2vdNA4XBYDC8SDEC2XBFMdeJnttsEYSKiWLIjpYKhJALVspNuZv3PzuA0oAQCK3QotxrbGtFLCYHRWuNrQIiy13RvgugQhcoOAm0FmzrP4ETBRxq2bHsbSbDEvW5YfqqGomFQAlZVsWL0JL1lR5//NaXsKetGikFvWNFvKRLlaU4k6phwk0SuQn+4aEznBoLlt0tvZw7B6txx2GlmAYKg8FgePGyWtaYwXBJmNls0VCRwLVluaqtVPaEhRAoFdN1pBPt+9PPk1LwrpvbuabGIpOwaahMUGVJpIoRWiHjCDcKsOIIR8VElrOCvdRIYtww4IDYzmG9kd1nn8UN/RWJY4BA2nRXN1OyHGLpAGLyL7U+dyyzdqU8JltohSXAtuS06G2tTWFZFsXNHYxlqiGRREpJXdqZvsh4sTHlJhtxbDAYDC8ujINsuCKZ6qrtnnCJZ4yb9sMYpTX5UghAwQ8p9vSSuverDP1DSN2nP4WsqiIuFvnYh/+eA1YduUQNE0JiqRitFSCx4xAlJBJN6K5MHO0Wp6igwKPuLjSSa888jQaebNuzwrMAoeNN/1kDEo1jlQeceMToWFFVlWSoEIMGS0VINFJrbOXOapTY0VzJ3o01fPFAD34MIlbYlmAkH5LyLLqHCwCrNi7cYDAYDIbLFSOQDVccM7tqKzbtQex+I6cG84wXA2KliWPNqaECAo0EElry5cptnO06yPr3/R43furPOXTwBAdFFRVC0acVQkhiIRFotBAoaa1a3/G4SlGyHJQW3NB5gEjaPLH+6hWcAH1enhgAAQpJPNndrJSmJsixoa6WbFgs9zoLgdKQJiKZdGlJWYTHj2O3tSETCW7d1cS3D/URFn1cFSJjwVheYssEDz03yOmH87Oy3e++ZT2qp8dEEAwGg8HwgsIIZMMVx8yu2k2dz7Dh+jfyveESsVKTeWKIYk3ClrRWJ5goTvBQ824ONmxGWw7tn/0+29trKbpJVODjorAc8EuKSEo0EFhOOY+8TINUoEjj48cu3XYTaLj5+COULIfHVyKOWUAcT/4IFCpWWFrhCPB0zLGBAo4UhFIQahsB5GyPdZZF/Z//HqMzhmL0jhXJSI0Ockw4KZRWxHFMTdrh1GCO2ox3blz4iSEe/e/PsaXzmUUP1Zg5KnypLvRKnmswGAwGw1IwAtlwxTGzq9Zpa+Omq9o5OHKKsUJA0pL4QTlmUYoU3aNFIjtFrAXjiXLjxaGhgKPDfdjJajJpBYUSYiILTgIty7EKvcLx0dvEGdaLAe4T1xABrz56Hzk3xWMbr132Nq0oKDdUyKlpeOcjY8X6YIyasX4GKtexmwkOpzdS4VmcHMzhWZIwVrTVJMjni3xabqFhyxb29B2h6onn6A5ShNKmVReoGRnBt1zCikqu37CB7z03Qr4U4YcxnmORz5e4N6qB1u1s6n72gjVoMP+o8L3tVfxih4Pb3n5BcT3fcxccW20wGAwGwwoxAtlwxTG3XaB9uIQlBZYoy8Yojin/SaNVOTox7bpO/h5rsLVm1I+wdDnLK1QE0kJrjZ5n4txS6IrXkbdSlHB5wzP3MJqs5JFNK5mwpifr2wDO7zSeekxlKUvteD9CSjwdY6FxBbcTfwAAIABJREFUJRROdaFlCjsOiS2H4T6frJ2gp2E3UsfI6p2k7xukrsJjwo8Yr26jOj6LZTtcO/gc16a285/ZEqUwBgGlyTHccd1mjgbNvKRyI+9vbb3gEZw3KjyOeOT+p9j7uW+yo8a9oAM935jx+dpJDAaDwWBYDUyLheGKZGa7wI7mSl7SXo3S4AcRAFJPjlYW07+cRynWKGERWg5WHLJh6Ey56m1Gj/BSkCjaxQBSxRStFN2s481PfoXxRIaHN1+/3EOdRIC0yv/BHHF8bhZ2KZHibFUz2rKQjsPmwVOIYgG3VF5gpyaHhuSx0BqSSZdEwiOQDoUgJuXZbG+uoDLjcYsa5t0n7+FnRRdOY+P0q4WxIp4cP1i3sZWGjo0c2rqXZ0eCCx7B3FHh+CVUGDJQ3zY9iGOxz50aP90zWlj8KTQYDAaDYZEYgWy44pFS8Dtv2sOrdjZSlXKwtMJTEXWxz/q6JI416SBfIDYRWC6dDRsmxfHyaBVD7BadVFhFAH58/38xnK7lwS03Lnubi0PAZJ2bQjDiZeh3q9hz6iC3eeNc17GOwPFwVETJ8ZBao6REislx07KctdaAH8ZIKfFcm46fegs3/Nn/oP4TH6OvoKjNOKQ8G6V0eRGghu4xH5lJoyZHfV/w/MwYFQ5AwkM6DuuGLj6IY+5zn8/x0waDwWB48WEiFoYXBLYl+b03X8XhnnE+993jdPWPgW1TCGHvphoe7xwhjOcZyTz5u7Js1Ar3oVvVk5UpxqngZx7+V07VtXPf1peucKsLUE6QzPrStS0aPIvS6Bi3RWf54cEDVH/or/mJT/9Prhsp0VfXgvu2t9M3MMqXnuwlGzvoyEem0uW1f0DCsc6Jz8YqnMn4QmttiiiGQhCRcCyKYYxS4AeKbDFclFidb/T0jbdczb6fu+6iGeTFjq2+HJiqIDTNHgaDwXDlYgSy4QWDlII97dX8xdv28idffIaDZ8aItaZruEBL2qYwMETBSVCwXPRy6ynmYBOxU5zmOdWGLxOM6zTveuCfON6wke92vHxVXmN+JgPXk46q1BoZBlQ31lIcH+Wa/mO4W7ciXAfd3c2OhgY6zhykpvVnUS0NjHzzu3xn3U4KOBDGeLYk5dkUg5hSqGaJT+37bCkM0FqdoHesOJ31ti1BrGKGsiVu2910UbG6lNHTq/nc55OZFYSLbfYwGAwGw+WHEciGFbPU+q21rus61pelczBHKYgYL4YoBULFXDV6lleePsB/X3MHpxJ1q/JaKUqsE6P0yToC5fIr9/0DRxq3cu/2W1Zl+/MyJ38sNAitcFRIUApmubLAdOOH1daGbG3lWG+WrcmY9q4HGa1vIfXGH+C6jnVIIegdL84SnzMF3w9v2kP3tjuw+nrxSgVUEDKWrubHho/zll96/6KF7nJHSF8O46fnY+bnuTE/zLrubpyGhulc9YWaPQwGg8FweWIEsmFFXKx+a64Y3tZUwf+897lVreuae0v7zHCentEC2WJ07kFC8lTTNjJ+jj4rvQpHXi5cniDN9/S1aAXv//b/4unm7XxrxyuXvJ3l7oOlFB2jXbz62IMkN21g5zt/jZ0bGxBBafqcTDV+yNZWPn1/V/nc77gVEYbs62jkHbeeO/d72qtnvcLMzuktnc/wsmtezWNhQMn2EEHETbkuXtn5MKqnB2sVheCV0nk89/MvtWb3ztv5qSPfwLlIrtpgMBgMly9GIBtWxIXqt3Y0V54nnjfWp+mcO3BiBXVdMx1O0dbGwIf+kCdPj5Lz4/Meq4TFo617CC1nRcfsEHK9fJaTqpk+UY+INL9579/xROtuvrHr1iVubSpMfAHxpzWWjonFVAcyoBVosOOIlF9gY2s1L/vLD2JXV593m7/643fyXLKBRx49ywPPDtJSk0AID601B7rGL3jupzqnw+5uOjftYftVm2h+5lGswQHW9Z2mww2wW1vRpRLa91clTvB8dR5PifAzw3miWGNbgva69JLE+Hyf/0Nb9zLw1lu56pqtJl5hMBgMVyhGIBtWxMXqt+aKh4NdY9iWoG6exy9HIE85nKKhgc/p9Rz60iEmYjlz/dosAmcVBBySEItQ2HhhiQ9962850H41X9v96mVucYGhHypGSQupFWqqy1lrhFZ4UUBoOcRS8lTTdk5nruL6e7v43TdVome4vmF3N5+8+yBPjmlGCwGj+YAwVmxsSF/03CulOTpc4sy7fpsHnz7D6QLoJweQO17FtS+3eNPtO1A9Z8neeSdjH/ytVcvcPh+dx1MifP/JEfonihSDmKRr0ViZYN/mukWL8Xk//0LQn6njaiOODQaD4YrFCGTDiphZvzUlZqYaDeYTD+WFXRqlFPkgxp9cFNZcnVzU68299b69tRXR1sY9xQwPrttOhWVjT8njSUF5HsuckucSEmIRY/GY3kky8PnwN+7k0Q3X8OU9r13WNhdEazJhgaybRgmJmNF1rIVAALEQ2BpiyyIbwXcO9yOA331Dx3TuuHPTHp4YjqixFE7GJVsMGc0H1Fd4pD17wfaJmS5uvhQxMOFTm3LYkJaIVIInRmOO5QUdnkt8thdrFTO3F7roWi2BPCXCXVsQRpqEIwkihWPLJYnxC33+DQaDwXDlYgSyYdkopVFKU5dxOTNcxHUElpCzGhDmioeUa7OxPsWjJ8vCCyDt2XznUD+7WqouurjvU986xoPHBilFMZ5t8fKOenjTe/nOoT6G8hGDQwXUQvYxLFscS2JeKg8xqit4mq1k/Dwf/vqdfH/jXu6+6vZlbfOCCEHeTpU7i9HYWqFkOYyhEAS2i5QWarK32ZYSrTWPdQ7zT4+mueFDf8jWeJyJEUn4xceICmN4iQTVTZsYygUMTPhUJd0Fq9JmurhKayQwNpqlqnuQCkeiNmylZ7TAzq1tsxYBrkbm9vkQnVMiXEUajUYIC3RMKSqX/S1WjF9J9XMGg8FgWDxGIBuWxaycqNIIAY1VCX7mFVumhe5C4uEVO9ZxtDdLfYVL0rVJexYHOme7djOd4ubqJAh49PgQX3qimyhWZXdRh3z5iR5q0i4pu+yxzmcYr8rxYnFKNzNBmqriBL/3tb/h4Y3X8V8vuWP1XkRrBBotJEJo1gXjOEGJnop1VPlZ6pWPtm3OOJW4QjMuU9Pp5VIYo4GoEHL3493cd9Tluk213FzhI8IAXBd8n/Yk2FaSO65p5oYt9QvmbWe6uAnHAq2IlWbYq4TSBAQBrTWp88Z+r0bm9vkQnVMi3LEEAoHWCgR4tiSM9KLF+JVSP2cwGAyGpWEEsmFZzJcTHZoIkJMO7eGecXpGCrxqZyOv2tVI79i5+rB7D/XhOpKGinNiauYt9LnieyhbKj9IK7LFCNeWpLzyYrVsMcTOZakuZZHJOuIVTMKbjxQ+EkWOFF00UZsf5SNf/SgPbd7Hf1z7xlV8JY1QEUKU5+FZSjFhJ4icNFrAaKKSUCWoC3K8vnCKluFuPtvxWkrxZJKE8mQ7T0oaKhNkPJv9J0d41eu28BI9zpNUQSqFo21u2VHPT9+8+YIibqaLm7YFlAJKlsMogrF0I1sSCbY1VQDnxn6vFs+H6Jwpwh1bUCjFpDyLIFRcv6VuSWL8cq2fMxgMBsPyMQLZsCwWyomeGcnz7UN9F2wguNgt9JniO1+KKIXlRopUYRysFEGocG2JJctZXOIIzxK4KiK0LMJVc5E1L5HHsVA8oK9mXXaQj3z1ozy4+Qb+7dofWp2XmCqxUBotrPLIZyGJhCSSFmiNrTWt0QTBumb+v64jvPK5h3lo642016UYK8ZMFEOU1qhYk3AlGc+efj96CzHv/+Nf4NDBE/S7lbQ2Vi1KbM4UkPlcgQhBbVyk2s+S2tBOhOBYX3bNROFai86ZIrx7uEAYK2xb0F67tBYLg8FgMLwwMQLZsCwWErlRpC/aQHCxW+gzxbcfxmURqRRuGGDJBLGQlMIIx7bIeDZX50Y4oxM4jiaYM4J5ZQieUlsQAlrG+vnI1z7KA5tv4F+ue9Oys8wLIXWMNbkAT0lQQgACITRoTRKFl3SofcdPUVf5Y2yXldjfOsHWxiT5IGYsX6JvzKe1JnneRYeVTHL1S/csbX9mCMivHejioe8fpTk/jEwkcGoqGMyHq7po7lJgnF+DwWAwLIQRyIZlsZDItS1x0QaCi91Cb60tL06bGJmgqCRxrLGkpFIqwiDHiJuhIuGRSdi8bJ3De971Vo48c4qeXMgZktz1wElylrfsY6sgT63Iclo3kRcp2kd6+MjXPsr9W27kn6//kdUVx1PlFMJC6wgJSNvCloJipHGjGIkuNyyEIa2NVTitVexUmus2jU6f/5TrsLXJIYw0g1l/VXK7UwIS1vN0zwSObESmkmghTVPDHOYOqzEYDAbDlY0RyIZlsZDIPdo7sagGginxNfWcew/1TU9M66hxUF1dPGtVgBDElg1IgtY2EkHE7soUN26qoeO/Pg/3D/Dl+9fTkB8mk81RW99KfWY7uXQ9y51Qt14M0CDG6KGB9UM9fPhrd3Lf1hv5xxveujriWOvztlOubiufM2lJXEcS6hgiRYQgK2y2Vaemm0PmO//bmio41pddcW53vumH+7bUl8V4PjRNDXOYO5hlNbqgDQaDwXBpEXqtlv1fZuzbt0/v37//Uu/GC56ZAxhmOsvzDV5YaGLazRUBf/WfT+BaglIMqQ3tTChJTcZjvBDi2hJR8lGnT6Ncl6hQZDhVQywlWkhKlo0WkuUKZCHKnccb+7v4nW98nPu2vJTPvuwnViaOtQatEFpjq5DQTszenlYkwhLJijTYNnk/xLUETm4CKQSJqESqtRk7mViTqXJTLPSe/NKrO1ZFfL8QCY8fZ/S9v4pIZ9C5HDWf/MSqLlo0GAwGw5oy7z9mxkE2rCoXik/MdSaVmj+vnO6oBcclUxijIpHAqU4zMeozmC2xsT6N0IqR0yfpTNWxcaSb0XQdOTcJiLLonBKeS7j4qybLVtnDk7qDCJutZ0/y29/8W+7beuPKxTEgtSITFCg6Hl4cETrlSXkOquwKWzY3h71c/8rX8NQXvslxlWQsVYWyJOPCxRHQUJsGabH/5AiHe8aRUkyfy9USrAtNsTu2O3vJ87pzPz+Xi0i3GhqIBwZR48eRVVVYDQ2XepcMBoPBsEKMQDasOvMtfprPmazLuJMdygJUDEUfpQTCsXHWt2PLlunMaxRrbEuURVvBpxRpcCxyqSpyidR0f3CZqXbgxeMSkiDAQnHVmaf54D1/x31bXsr/fvlPrkqsQgmJ7ybx4oCrxrp4qmELWmkCyyG2LIS0eLimgwP3nSahKxlOV1PtZ6lubSKXixiXHt1jJarTLrFSfP6+kwznSgs2hSyX52OK3XJYyNleKyd9KcSDg8iGBuxNm1C5XPnrKrPwz2AwGK5kjEA2PC/M50yeGS6WO3zjiOjQYZTvo1LV7Hv9NnJ+NCvzevX6ak4N5sgWA/xIErvlCW8F6RBrMUMcM7nwbXHusU1EhM2AqGNQ13LDqSd437c/w/1bbuB/3fz22dtdCQKIY2wpubb3KP1OBbEQ9FY348UBykkRawhiTRKwlCLnpnFjTaBBhYq+sSLDuRKWFASRorU2tWBTyHK5XEcnL+Rsr8YxrxS7rQ17/Xri7m7s9etXZZqgwWAwGC4tRiAbnhfmcyZdR9BYlWBwcIIwtpDJSq4eOklHfB275sQ0tlTbvOvvH+XZwajc+etWQxwxnsgsW8TWMs618jgH9HbGqOCm44/y3u/9Aw9suZ5P3/yO1RPHWiO1oiU7hNXchFcqctXZIzy8cS8ohdaQcC1KsQIkIplACEFJOvQXFXF5+nHZOVW63HrH2ri8l+vo5NmfH02+FDFeDHj0xNAlj1qsxTRBg8FgMFxajEA2rCoL1V3N50xaQvIzr9iCCAKOfewB1p3uZFuNi9vejpiMaeys8whPnOTAH3+eML2HTbbFaF0zIxNFtFIIIdFT2miedogLkSPFEFXkSfDqZ+/nXQ/8Ew9tup5P3fKzKLlK4ri8Y0ityzGSkWGasgO8/MgDtI/0cNfeH6S6OIGMMpyubgEBtRvbKI0UKBbjcu2bYHooSlttilJUHvu8Fi7v5To6+dznR3F6qMBYPiBSmq8f7CXnR5c8arHa0wRncrlmrw0Gg+GFjBHIhlXjQnVXCzmTu1qqypnlv/zgecJ6anvR8eOccZrQ117DmLIYHS8QCYm2nLl7MBk/vrB4qCTPBCkC4XJQd3DHoXv5uYf/lYc3XccnXvlzqyyOy+mKSFqMZGq5dqILsWULD7mVtI71cWPnAQ617kSVIjwBwrbwY43jOiRjSLoWuWJE0pOEUVkQp1ybjQ1pTg3m18TlXewAjeez+3fq8/PAs4MM5UrYUlBf4dFcnbhsohZrweWcvTYYDIYXMkYgG1aNqLu7LI4bGoi7u4m6u6ddtbnOZHN1EjSz+o+nHhsPDFD8xjdxtnWUt9fSQmPnIEGkmUgkcVRIZHnLGphXQYGXyWd4Vm/gFM286eDXePtj/8n3N+7l46/6eZS0lnfwU40Z84hzLSRojYhjnqhs56m23VTXnEEqxe6ew/zCY3cxWN3Etrf8BvbmTZwdKfK1gz08fmqUYhCh0OT8CEtKSqHi5u31vGpnI/s7R0DA9Zvrpi80ni+e7+7fqc9P2rO5+/FuGioTs0ZqX+pFhGvF5Zy9NhgMhhcyRiAbVg27rQ2rrW1aNM1drDTlTG6vdfnk3Qd5YiRGCTHLFdNDg5x96U2crGymr7aZtk1b2XzqGXZuW0/7xkb6+0vlcczztVQsIjOcJclhvYmz1PPj+7/IW578Mo9suJY7b30nsVzBXwchSJTy+G4KoRV6rtAWgshLoIEo1CTikFRQ5FDrTm72e7itNUndDTsRiQRSCEbzAXUZj/FigGMJolizs6WS97xuG987PMDffO3otKOY9yN2tayOWFqsK3yhi6G1QkrBjVvrue/owLQ4vlwWEa6Vm365tooYDAbDCx0jkA2rxmIWK2nf59EP/AGPJXfhOBa0b8Rx5bQr1nLPN/nkS9/GkcYOlBBIx2aX084vF57Cf+4EqqINJaxJp3bKQ57TezyPi9sshhjVFfgiwRkaeccj/84PPf1NHlv/Eu689RdXJo7LL05Ddpie2gRqPqGuNUVh46gYKwoJKqvJ5BRkKsn/2m9Rd+seRCKBUppHjg8xVgxpqPCoq3ApRQo/iHnzDe3YUvL4qbVxFJfiCl/sYmgxLCdbezkuIlxLN/1ybRUxGAyGFzpGIBtWhZkO2oWcxKi7m+7xEoPVlYRIxEgeIS0cW3BmOM+/2dt4aGMlSgiUtLDQPNSwncO5RkaS5e+fa5eYz0U+/3sOIbvFKXpFHYf0Zt754P/h9Ue+y4G2q/jr295FZK3wr4HWgKa/sh5LKZQ9Q8BPa3iBUAptWWgVk2xpxBfNFENQTc1o10NP5k0feHaQ0XxAthhSnXJZX5dilJD22vSaOopLcYVX2tyw3Gzt5biIcC3d9MvxgsBgMBheDBiBbFgxS3UeVV09RWGTIMZybDSaQimmayjP04M+ViKBihWWECilII4YTtcgVQwahI7PjzDAgovzQhy+r3dT0B6/fN8/cNtzD/FE627+6tXvJjpvod8yEOVfAjtBY36IQqKCrJsqi2Otph5ALCVuFOBKQU9RUwhCUp7FXY900TmY41W7GjnQOUJLTYIwVozmA4ayJWwpuWVHw7QoWg1HcT73dqmu8EqaG1aSrb3cFhGuhpu+EJfjBYHBYDC8GDAC2bBiluo8Vv3020l94xihFsSRAgEpz2K8GGJLUdaVCKSgnCuedIynogsC0IuodNskeinhcJYG8jrJ+77zGW46+RgHW3byl695D6G9CuIYEFqXjWIpGE1Wn/O1Z+2jwIo11cUJ2jva6Q5t2utcKpIOWsP+kyOkXIt8qdzzXFfhUZfxGMz63HFNMz9982akFMtyFOcKxQu5t89Xn+9aZ2vnXrRVf/xOnh0J1qQqba17kBd7QWAwGAyG1eOKFchCiNuAeye/7NBaH7+U+/NiZqkO2vqmappq0zi2pBQpPFsShIqdLVU8fWqYTDHLiJ0kUoAQJFVACQsv9MkmKyiPypgjbuYIZoGmXoxRwqU3ruU37/k0N3Q9ydPN2/nz1/4yge2u2vFrRHlhnpBIrfBQxLFFJC2EitFSIlWEq0Kqgjxn8gonIahIlvdBCFBa88iJYfrHfco2uaDCs6mr8LhhS/20mFuqozifu390uHRB93atF9vB2mdrZ160hd3dfPLugzw5ptesKm0te5ANBoPB8PxzRQpkIYQLfArIA+lLvDsvepbqoO1ormTf5jr2nxwBIIw012+p43Xbazj02e/zhK7AzzRQcjy8qER9cQwrLJF3U+S89GQGeY6DPP3nsnzWCA7o7Qil+R/f+Fv29jzDoaZt/Nlr30tge8s7ThXjRCHKslBCooREoJCqPOou1hKkRTAZA0HLskMqBEiLkpBkqxtwPIdozqCPIFL4YYRtSYpBhNaa4TCgIuGwrali1n4sxVGcz93vKaYveTPCWmdrZ160dW7awxMjMbUVCVOVZjAYDIZFcUUKZOA3gFrgfwO/fon3xcDSHLQpF/Rwzzj7O4fRGva1ZSh99z5+4pt/z42pBvoqGwilja0VbU3VtD90D1+8+nbu2X4LOS9N0ZlfhG8T3VSIAo/rbQgFH/7qnVzV9yxHGrfyp697LyVn6eJYqBg3DtkwcoZ0UKToJunLNFB0E+V4hZBE0kYIqPEkyfFRClrQl6nHVTElLJQQIGDAqSTjx9y4pZ5TQ+cGfbRWe5zuG0crTcq1ynVwscKPYo71ZZct5OZz91uHS5e8GWGts7UzL9qyEy7q0W5TlWYwGAyGRXPFCWQhxAbgd4FfATZc4t0xrIDvHukv52DjmG9//Qx7+p7jbb7P1sIptg6dAikRdXVIKokr0lzTc5j7tr60XP+GnrcLuYiHQ4SMY/7gK3/NzoETHF23hT9+3a/hLyCqL0QyKPCOh/+Vb+6+jUxQRACpoIirI1K6RFELxp1MWQAD/bFNRaISNyrhqggmxyODmPxzebs/fGO5sq1ntEBLymLso3/DX2auBTeFnU4Raw0IlNacGckvW8jN5+7vaPYui2aEtc7WTl20tfWMY4keU5VmMBgMhkVzxQlk4BPAU8DngY9c2l0xLJejvRPsPzFEjYwRUhMUxnl63VaOt25D+j59leto8kfpaKogPnIECgU2k6V5op/BTN1k5nhqa5oEJXzKHcd2FPKnX/oztg51caxhE39y+6/iu8tYOKU173j437j1+CMcbtnJ0aat2HFMQmp26QmOq2oKXiXCspBTU65VjFaK2/KneWLzXnJ9g/R51ajJSIgGwljRM1rgjqtb2dVaRXj8OMOdz7Br50YecNIEfoSi3FYxlg/5/rEhXru7ednu6lx3/8XWjGCq0gwGg8GwVK4ogSyEeCPwRuAGrbUW4sL/oAshfhH4RYD169ev/Q4agPnrtWbWirWkLU4fPUXYdYYwNwqWJO+lGMfin2/6CfxiUG6xQLGn/zhvl8eQUiKV4kee+Aq9letQQjCYriMWkq12L+vFAA/oq1ER/MV//wkbRns4Xr+BP7r91ym4F3EKtZp3Cp8X+nhC8y/Xv5nummZsFRNbFu19x/mlI1/lYz/1B/RlLeIZWl0hKNgJnnLqqE/ZDDpJ1GQNnACUgnwp5psHe7l9TwtSinJ3dFsb7z3yZSauegtPuS04AmzLoipp0zmYX/W87IupGeHFdkFgMBgMhpVzxQhkIUSSsnv8Wa31gcU8R2v9GeAzAPv27dMXebhhFZivNSF2XP7ki89w8MwYtgC3r4e2kbNop4K4kONsZRNZmaFkOYy4GdJWgZbxPpJBkWcat3K8swFZ3UqfW0HdxDANE8OcbNiAFhItLXp0AxE2OlL89Rf+iLaJfk7WreePXv8+Ct4C4nhG7AEhz2vBKC/z0zzd2MHTLTtIBUW8KMCNSpypaeG0SPLmez7Pyb0/xrCbQanJpwtQGo5nmmicCLGqKrAKEfHkp09KgQRODeb56v5TWONjrN/Syo6P30nU3c3e0zHH9/dg25KalMu6So+hXGDysitk7gWBUprDPeNrUvtmMBgMhiufK0YgA78DVE/+brhMmduaEJw5w58eLPDtw33TqYiMdoi9atqHujhRv4HRdBUyjrHQhMIi62XorFtPulRA6Jh/vOFHybdvJDueJ+umUYDUinprnH7qKIgkZ0PJR7/w+7RkBzlV28YfvP595LwLFJzMdYzniGMQ+G6K73S8HC3K7q+lFG4cYEcRfZXruKnKwvWLxHam/CwNQkhsC5wowB3oI+NIRhP1WIBjS2wBCsiVQj7734+TKhWwnKNcf8vVICVffvIs2WIEwEQhpFCKqEg4Ji+7iix3ip/BYDAYXjxcEQJZCNECfAD4GFAthKie/FHt5O/rhRCx1rrzkuygYZq5rQnHZCUPHDtNGJ97zKibpqQFP3Tmq7SM9fH1nbdi65iJRAUojRAQWTajqSqkUoyma9ElgfYqpoVsK4Pstk7zsPLwA5u/+c/fpzE/zOmalrI4TmRWcBTnRJIWcrKoOJ4eVFJKVxDv2s3fFhwGEpU4KiKUNpYoNyRINCiNZ8GostGqLIp1qIgsQVXCIecHtBXHqbBAFfI8eKSfQFjEsca1BZHSRLFmKBtwdWslWwoDaN9b0+EdLxZWMsXPYDAYDC8OrgiBDKwDPOC3Jv+by73AOGWH2XAJmduacPcjPZQiNedBkpKTIJQWvVVNFLxUudbMskFrFJIpkWqhCLDKX4tzKZkuGimqBH5g8/H/+DD1hTHOVDfz+3f8BhPJVVx8NSnItZRoDaHjkUl5FG67naOPdSGkxFIxStooDaDRAqp1CGHIRLqahCvxbIu8H05uE1KeQ8aRaN9HJhIEQlKYvIpL0PTfAAAgAElEQVRIeTZRXO5GrvAsrn7gy4x//pGLjvE2LI61nuJnMBgMhiufK0UgdwJvnef7Pzr5/fcCXc/rHr1AmW+B3VKfM7c1QUxGK2aGwG2tGE3XcKammerCOGPJyvLPp6IPk0+IkNMiVaLpEF2coI0Im1yQ4BN3/S51xXF6qppWXxzPotxjjLTL0r2mhrw7RCAssKzpY5NAwrYIEtWMVFUSB5r6lEtLXye5QDGcqOTlu7dwdNDHad4FfgkSHt54AMRMxAqtwbYEsRKkLWjs7VzUGG/D4ljrKX4Gg8FguPK5IgSy1noc+I+53xdC7Jn849fNqOmVM98Cu4uJ5Is95/rNdfzfh0+RLUZIUc7pShVTlxuZFJaStvFeIstmzK6a7hMGygverHMf0UrybBD9jOkKsqUkn7jrd6nxJzhbuY6P3PEbjKXW2v3TyDBEhUXiuInQS+IoRaTOif+qpMOOlkpODeZoSdjEOUWSiFygSBayVIQRN3/lftJveRcHusZRWMhCzE3bGtBa8fWn+siXyhnktGdz0851bHvWXfQYb8PFMbVvBoPBYLgYV4RANjw/zDeW+GJu5YWeo32fDn+QH7iqia881UehUEJoTSoosHWiB60Uge2Qd5P4jocdhwS2h9AKPVUJUd4SIBgTlXxPX4vth3zyrt+mqpSnt6KBj7zhA4yml5Ou0czMG18IKw6RQF1cwIsCxofGSHkWQSjQfgklJBaa+oo0XUN5hkfzjKmIQDqMWhZWZh0pO8Mrew6yeeQ5MjUxmfQ6iCKuTQVcdc16hJfgtj3NPHZyGAHs21THrtYqxG2LH+NtuDim9s1gMBgMF+OKFsha698Hfv8S78YVz1REwmpoOG8s8UxmdhlPVWPNN8p4apsjv/Jeos5O3rZpE6/86ffwwD9+ERIpTudCjtdv4Kn6LRRtj6F0DUpIlCzHK4TW6MlIg0XMXnmMTt3CENU4fsAn7vodqkp5+ivq+cgbPsBIumaZRy7mqXdb6KESBQQahJtgx5ZGDgyfRckS/kSerJtCKIUOQkZzAVIptLBIxSVCJ0VDdQbdl+flpbP8n123s//BfgIsnJFBBgaP0yq6qP/Ex9jTVs2etjlifwljvA2L48XUA32lMt//b8xFjMFgeL64ogWyYeXMjUjU/NVfEA8OnudWRrHij7/4DE+dGcOWgpRrsW9zHe95zbbzRhkDhCdOUHroYdCauLePne/8BVryz3FkAL501VvJWS4xEtCgNdZk9CKWGiWt6deVaGxibGIyfpZP/vv/oCIoMpCp4yN3fIDhTO2cA5rRb7zwUc8ORE/1zyk9mQGZzD1PDvhAa+LJfRpKVONZgi8c7CfrR+RLCuWmEUrhChjyNZEWJIkJtMSSEmVZJBMurG/l2N538u2DAwSjhcnX8PjOup28/NhxbjxxEuG5xik2vOgxVXwGg+FSc/74MMOLirkRiXhwEGfr1lkCTSnNn3zxGb57pJ/xfMBwrsREMeKxE8Mc7Z2YXpQ3W9RN/iOmNVprxj96J9HBgzxp15O1PGJhlR8iBEhJLAVSa+y43ORgEwGaUDg8rPeQL7p88t9/h4qgyGC6lo+84TcYrKg7/4C0Zrb6nY/JfRNl8WvrePrr6X9745gqP4sVR5NCViClmHym4GR/ntaaJOvrMtRVpbATLv+PvfcOr6pO178/q+yettMbCSGU0KsICipGRpwRxR/CeMA+oiKiokfEeUWwKwqiA4zM4CigzFEZ0WMZFUc4dulIh4RASAjpdfe91vf9YyfbhBSKOI6yPteVi2Stb1lrJ7rv/aznuR9HbBSKEhqhRkciWS1gswFgkUEKBKjw6LglFZOkY9ICqAjckonvU3tR//zzVN91D1V33YPwek/q92fwn4fwegnk5Rm/wx9Bcyu+hEgrToc5bMVnYGBg8O/AiCCf5bSXItGcvSV1bD9SgyyB2aSEBK/Hj6pI7VpjmbK7oA4fzv5yD8cSM0gsPYTmTGdnQnbYT/gHJCShE+euBqDSEcMQ8wGqiWS3yCLKU8+Lbz1EpN9NhcPJnN/eR1lkQts31Cz63DaNUWIk0HXMevCHfGepMbUDQJbxyiYEoU+RIR0voQuBJkICvKjaTSAoCOo6viBYAjo906I5VN5AZYMf1efDjYxNBHEXlNO/ogC7wwKROaAJJEVBjohA8gexjDgf7ZX1KAkJBIqK2LEtj1JHnPFo+RfG6RS6GrTGsOIzMDD4uTEE8lnO8b7Fbb2ZF1e5UWUJWQqlREgSaLqg3hPA5/bi2b+fg2oMR11aWNAJIfh77o1sKfMRlCSOJtUTBPQWRXE/RJklwBQM4Dbb8CtmjolYaokgyl3LC6tnE+l3U2mPYe5l91EalXjqNypCraPNWgAhSVgCPlShYw36aDA7CCgmkGREk9dyY9aF1PiNLkJ2dUKAKktoCDw+DZtFQQ+ExnoCOi5fkKzECBStlv5Fe+imeJErykjx1dIjxsTeGokPBvbDp6lIsoLQBA6rmWGDs1HWphMoKmJlz0vZtbUWXaozHi3/wjidQleD1hhWfAYGBj83hkA2aOVbfDxpsXbsZpWgTafWHcAb0NAFmGSJ/1nzLW/4fAiLBblTJxRFYVCnaAZ9sIKNtl5Eyxq7zPF4FXP7+wsdm99NRXQsQlEQskI+6US7anjh7YeJ8ruptkUz97L7KIlOOs2bBAShYsDGqJRJkamKTCKoh87JUuhYQBPoQic26EaTLFRgCn8okGWwWxTsZoWKBj/+gI5AQlYkJHS8AQ2HRcXhsJCrVpNdsBM5JQUpyoZ2tISc9HQuG5DK1/nV+IIaFlXh/O4J9OqcgPTiQnZsy2PX1lpiI61Gl7dfICfzRMbgxBhWfAYGBj83hkA2OCE5KVEM6RLLxvwKAhWV+CQzTq+LLg31NOgyBRFJZLlKMelBvEh8uvMoh6RUPCYrQb8Xr9zWn5lA0UMd9tKri/EqJrKdFciS4EvRD2dDNfPXPEqUz0WNNZK5l93L0Zjk07yDUEqFkCQCSii9o97ioF4IIiSIibRQUe9DExDUBLoAWZKxpSSRFB+FpcZLt+RIom1mEiItnNM1Dl0XPPnuLsyqjMUkU1Hno8rlx+PXqGrwMyQ7nqE3zkEvLg6LpKYo/Z1mC5c0sxjrEWtGO5iPmp5OqSMOXaozHi3/QjmZJzIGJ8aw4jMwMPi5MQSywQlperPaGRXg3c3/x9bIdFLqK5CsFgIOJ0IISmyxBKp8+IUfTRfUxmShA4rN1qqLHggkXcfpqkGTZTRJoiw6GQ/RyEIntbqEJ96fR5SvgVpLBHMvu5ciZyqn4lv8w1YCU9BHQDY15kzIIacLSQZZxhUEzRPEqsq4AzqaoLHdNRyu9uHGRbTdwuTzs1oIVF0XjOiRwKaDVfiCOlE2E/0znZzXLYH0uB/ezJVmkfmmKL0EYYux43NWU2c9ajxa/oVzoicyBieHYcVnYGDwc2IIZIMOad5Guu+ArrhMNewNJoRUnt2OPSsTShvwBkD1eRGNqRQ6EiZ0vKqFxpq2Rn/jkMiVBNTao4jU6tEjrAhk6oSdxLpyHvvgWaJ8DbhNVpaMvIGSqERkXUMAonmB34n8ixs3NukamqwiC4FOyIlCazZXQhD0+ZCbPCqEQJdlNCQq630MyGz9aPfHRriaPF4P7z5IVLWf7o05q121WuPRsoGBgYGBwc+MIZAN2qWtivyhCx5h6JotbCn1oiMhC4i3q5TX+gnIaig9AUFAVqAxt9eiyghdR/cH0GQFWdfQAUnXSbNUkyDVUKY7iXA18MT7zxDjrafBbOfVcydwNCYZmZCgVXSNYFjYtiNEhd74b6PzhK4TUM1Ygn40WUFXFPTwEqFvdF0QREIBFF3DhI6mSQRklUibhWHd4zsWvidylTuOFh6vmoboOpq+FQXclG7B3KkTd2RbjEfLBgYGBgYGPyOGQDZol/Yq8u+8ahAb/vsRjtb6SI22YL9zGo+v/BYtGKTCEkVAkpGEwISOSZWIjTRxcXSQvH9tZX9CFja/h9LIRAImM7voglX34nA3MO9/nyDWU4vLbOOp0XdyODaUuxuQFYQkE5QJpUa0IpSygSRh83vwq2YkIUJiVwI56KdL+WEOJmRSr6jhKLQCRNpMWE0Svio36Bou1Ypqs2NCByETYTXTKdbRakd/UOOBv29lb0kdZlUmxmbinOz4k3KbaO7xKkkSIiKbXdXJlI29hQSrtUUKhsHPh9HJzcDAwODsxRDIBu1yfEW+FB/P99/upLjaQ1RZGRfYBaKgnGjVywUjerNx/zFqXRoBv44igwhqRNVXo9YGSNiwkdgjRzgalYxF9ZFjOcIOuqILCbPbz9P/+xRx7hpcJiuPXnoP+QmdUfRQMw2TruGXZRQ9iNaUS9ysTbSs6aFosRYkreYoRc50PCYLAdWMFwEmK+VR8fz3V6+yJ3sAVbEpHPKrFDlTcOlmGlQVyRzKldYENPg0ZBmibQojesTTI9ZMIC8vXHQV1HRu+PM3HKpwAaFAdIMniO4tYlRXJ727dGxD18rjVVERFgtH3Rq9f9Lf6OnRPM3mbCk603XB4o/2sOlAKcJkQlEUw27PwMDA4CzCEMgG7dK8Il+Kj+f5R5azXYpGN1tQmqUFmBITuZUyRmX3YP2Sv/OJuRMO/FgDfqx11dRZI0jYsxV0QVBRCERHkywVY9F9mN1env7fp0hwVeE2WXlszD3kJWaBCKVVRPtdaLKKX44E5JAvsQAQOHxu/IoJRegoWhCvyUKtLQafYm4WaQ4J6bKIeEot0fyX7yD6zo3s8pp56qLbQAhkIKCHbN46Jzjwazoen87tuV25rGccNXfPaJFm8vGeSoqq3QCoioSuC9y+ANV19exfuJRe8+7vUEj+kjxez9bGF3sOlbPhi+1Eu2uRrVbU3r0Muz0DAwODswhDIBt0SFNF/rbPt/KtiMEUDGDzeoju3YNdycmUjrkB/f4H+MwTQX5MGp0O7KBHXAP7Erviw0RtRBypdaXoeqjLRmlkAn7ZQokeR6S7niffe4YEVxUe1cLjl97NgcTsxo1BSArVjljUYAAkGUvAjcdsC3e+a7A4QJJwuqpJbKjgiDONans0utI8DSMUZdYkmYK4TtivvQBTdhf2/OVDfMjY0fApFhA6uoCAJsiIi6C83ovFrKAXF7dKM9lbEgAIRxLlxkYiAdVMYknBCZtDNHm8bsyvxBPQCGqCfhkxdE+O/Il+i6efLnC2Nr4ozC9GCwSRzeZQy2ivDx3FsNszMDAwOEswBLLBCdF1wYqd1ZTbnShCQxIQ06AREeXg8OFSnokYxuGkBISuow3pjqoHiXNVU2OLRtY1TMEAL+dOIj2qBqsI4hcmYhpqeOTD+SQ1VOBVzTz5m7vYl9RaeOlI6LKMJejDbbLS1N8OGv8RArfZDpJMpNdFeUTscSuExgpZprOvBtuFF4R+DgQB8EgqwYAerrOrcflJj7WFI7pqnLNV44ec+kpkSUJCoAnQdRCSRHb9Mbo7zajp6R0KUlmWuD23G5X1PrYfqUFVJA6Vu3jpXwd+kkf4LYoChTil7nxna+OLjOw0FNNedLcL2WoFqwXZrf1HRvkNDAwMDM48hkA2OCF7S+oocuuoQkPVQsKy0qvjl31sLvRTYE9ADfpBQFA141dMgIQmSQRNZrwmC+WOKGLwIYI6cZ4q5v5zASn15fiUkDjendK97c0liaCsIqlqKEwrdCAkTkMGbRJClvErJlJrS/ApJmpskaEUi2ZWbjqw7/c388neCiIXzqNnfh7mi3vgUmyhbQhlbngCGkervYzokUBOShSSLLVq/HBpvxT+sbGQ/NIGZASyIpEeY+PZyaOwZ96IMFtOKEj3H6vnUIWLzvGOk+6Yd7q5wK2KAk+hO9/Z2viiZ+cEho7sH85Blt2aYbdnYGBgcBbRliWAwVmOrgt2F9fyyZZCvv92J4XHarDYLEShEVBU3CYbPk3gqm1g7cE6grKCXzXjM1nC6Q9lkXH4VQuqHKQ0OhG3bGdrsBt9Du1mzj+fJ7WuDJ9i4qnf3MmulB4dX5AkEUAGKeRULAmBLASSCH2vIVFtjaIoJhWz5kcVIUeLsBOc0JGF4PP8Ov769kaWxA7mu04D6VaWjyx0VBkUWSLaphLnMHNp/5QWYrYpzUSyWtF1wf5j9Uw4N4Nrz+/MuMGdmDW2NyunjcDRoweS1dpCkCZEWnE6zGFB2kSrQr1mHfPaoikXuPque6i6657QY/9mv6u1O0rYXVwbSmU5jlPdq9XL3+z+zxZkWWLamJ48cPUgrrsgm5ljexkFegYGvxLWr1+PJElIksS8efPaHDN//vzwmPXr17c55tVXX0WSJJKSkggGg22OufHGG8PrtPX16aefnvZ9VFRUcPPNN5OQkIDdbue888477fX27t2LxWJp95qCwSCPPfYYWVlZWK1WcnJyWLRoEUK0fM/56quvuOqqq8jKysJutxMXF8ewYcNYsWJFq7ElJSX88Y9/5De/+Q1xcXFIksTcuXPbvcbdu3czYcIE0tPTsdvtdO/enQceeICqqqrTuucTYUSQDVrQ9Dh+U34FgcIjSH4f6ZoLObsPaf4aLD4oiU7CovlJdFdTYneGJopQPFfICgAaChGSh/PknewVmRwRiWRWHmbqV6/hCHjwKyrPXDKNHak927iKxkK85o1AGv/DErICQkdrTLVQhI4p6MdjslCvRKBLErKuhbvlSQgsegCfYkYEg1jc9UR769iV3J0xrjzyLSZ8QkKSIKgL/JrgnC5xYSHUPGrbXmT4sv6pLcYf3n0QTdNaC9LSWrp5ylHT00+5UK+tXGClS/ZJpU78kooC/5MwOrkZGPy6sVqtrFy5kpkzZ7Y6t2LFCqxWK97GYERbrFixgqysLAoKCvjnP//J2LFj2x27fPlyZLl1TLJPnz6nde1er5eLL76YgoIC7rvvPpKSknj55Ze57LLL+Pjjj7n44otPab1p06ZhMpnw+/1tnp86dSrLli1jypQpDB06lE8++YTp06dTVVXFww8/HB63f/9+gsEgN910EykpKXi9Xj766CNuuOEGtmzZwsKFC8Nj9+3bx1NPPUVmZiaDBw9m7dq17V7fvn37GDp0KDExMUydOpXExES+++47nnvuOT7++GM2b96MoiindM8nwhDIZynt5ciGo5+yRtBVje7zUWhykLF/G0fM0bitZkDg1H0oZhOKpiHJessOd41itgEb+SKdY8JJtKuG+z9biiPgISArzMu9g+3px5maCYEkdIQsh5qJyEo73fKkUORXBEM5yopKUFbCNnAmoaMRahIiJAlhtoIOih7EIkLSWtjs1KoJyK4GZNWEbLWFO/4hQkI3kJ9P3YKF6CUlKOnpHJv1aLupCjkpUew5VM6+hUvxVdeipw9GRGSHLNwaI94RLy+humAnSno6PV54/pQ65rWVC7znJFMnmooCje58BgYGBj8wduxY3nrrLbZt28aAAQPCx3fs2MH333/PxIkTefPNN9ucW1hYyPr161m2bBnPP/88K1as6FAgT5o0CVU9c5Jr6dKl7Nixg3fffZcrrrgCgOuvv57evXszY8YMtm/fftJr/f3vf+frr79m5syZPProo63Ob9++nWXLljFjxgwWLFgAwC233MKECRN48sknmTJlCikpKQDcdNNN3HTTTS3mT58+ncsvv5zFixfz+OOPExERAcDgwYMpLy8nPj6evLw8unXr1u41vvzyy7hcLr7++mv69esHwJQpU4iMjGThwoVs2bKFc84556Tv+WQwBPJZRJMoPlLp4tsDFRSUu9BpGXlsehwv2ywgyxAMIqwyQ8rzGGuR2CKi+L+kXnTqnkl9ZS1SlRdLwIfTU0eDxY7bZCOBaqqUGPySmXyRSlx9BXM+XkhiQxU6EotG3si21F5hH2MAhMAS9AISaGAWGg2SFSEd/4lQIOkaSBJ2zU+9xYFfFyDJ6JLUmLOsIAsREsdAUIScJqKFH4csIMaJKS0FsfMAsTYfVlcNwfgsbBF2PH6NkvI6Uh6bRTA/j0BJKYXDcznqlqjZdghNb52qcKTKxWe7jrFxz1EC5s7IaQqSz0dFVQNYrchIDIxVyCrYGY4A68XFp9Squq1c4OKq6nZTJ5oL5B/bFtvAwMDg10hubi5ffvklK1eubCGQly9fTmpqKrm5ue0K5JUrV2KxWBg/fjxlZWXMnTuX6upqnE7naV9PRUUFFRUVZGRkYLd3/ITvjTfeoHPnzmFxDOBwOLjllluYPXs2e/bsoWfPtp7QtqSuro777ruPmTNnkpWV1e5eAHfffXeL43fffTerV6/mnXfeYerUqR3uk5mZSTAYxOVyhQVyZGQkkZEn595UVxdKUUxNTW1xvOlnh6N1Q68fi5GDfJbQlDrx7Pu7eXl9Puv2lFLn8ZMQaWmRI5vitOHzBynZe5B6n4auC2RdkBJpxvb7CWC3E+n3UJhXhKuqBpMWwKr5sQZ8RHkbsOo++poO0V0+AkIQ4a3nwU8Xk1Zbii5J/OX8yXyXNRghh1pHh8K2odCtqutIQhDtref8hkIidW9IDDdHhBwjdEmmzmRHQ+aHZGMJJJmgag45X2gBevoqufn8DEb3SSEmNZHa9C40dOnBkB4pDLD68OsCr8WO1W7FblaQJYkkfx1aURFSaiqv9f0tS6IHsKbTuawt8lJR70M0trNuSlUIBgWbC6qIczqIVzRiPHUIi5WJ52Vx3YgsZo7txZ1X9MeUno5WXh6OADc9wh/dJ4VeadEnFKzH5wI3T51ofj1tpU6c6l4GBgYGv3YURWHSpEmsWrUKTQu912iaxqpVq5g0aVKbKRFNrFy5kssvv5zo6GgmT56M3+8PC8m2qKysDAvg5l/NWbRoET179mTDhg0dXreu62zdupVzzz231blhw4YBsHnz5g7XaGL27NlYLBZmzZrV7phNmzaRlJREZmZmi+NDhw5FluU292poaKCiooL8/HyWLl3KK6+8woABA0hKSjqp6zqe3NxcIJTTvWXLFoqKilizZg3PPvss48ePp1evXqe1bkcYEeSzhOaFY5oeihrXeoI0eINEWE3hSOj+o/XUuXw0KJHocZHYAj5GlWzni6hk1q0vxe3siy5LqLpGqruS37iKkCsqUbweaqyRfNh3NBtFTxp0C05XNQ+tfZHOVUXoSLw2ZDxHnKmYtCBCkggqSqM2llBkCV1W0CWZnmUHGeXKY/vwPgTcQfzBIJrS7E+1MVKsQ8sodOO/Jj2IPegjUgS496aL6JuTjubxtGiPPXjSAyypGYvrkJtyTcCxBhwWlcv6p9C7fyY16ensrfazs+sArClJ+FULdpNMvTfkcmE2yeFUBVWRQpFcRcXUpzfC40UOSljsVkb3SQlf9plyg2j+JCAz3kFBWQNC4pRTJ87GDnkGBgYGzbn++uuZP38+a9euZcyYMXz66aeUlJRw3XXXtStUv/32W/bt28fTTz8NQKdOnRg5ciTLly/n9ttvb3NOcnJym8ePL1w7GaqqqvB6vaSlpbU61xRRLS4uPuE627dvZ/Hixbz11lvYbLZ2xx09erTNvcxmM3FxcW3udeedd7J8+XIg9HQzNzeXv/71rye8pva4+uqrmT17NvPnz2fw4MHh47fccgsvvfTSaa/bEYZAPkto7mRgNSuNDeZ0vAENh0VtEQm1Wkx43D6EAI/Jyv7EbOqEgku1ogECGU1RKIhMptAejyPKQ7JaRY0lBgB3wEx6zVHu+vwVMquL0SSZDwf/jo2Z/UmuKwcEmqSEiugkgSpJJFhBq3fRo2Q/d3z+KkpcLD2y8/nalo5JBEGX0AkV0+mSTGMVX/j+1GAAFBlJUYnxuojHT61Q+GhLEUpkJNnuMrILdtI9IQEtv5SN985lU9xgshWdQNceeII6/qBgVK9kFJuN2BcXUvvFHiq3VBNoEAg8SEioClzaN4X0eHs4VWFvSd0PRXCyAnY7coO/VSS3KQL8Yzje01hGoktiBMO6x9Mp1nHSqROn0yHvdJuNGBgYGPyn0q9fP/r168fKlSsZM2YMK1asoH///vTr169dgbxixQqcTie//e1vw8cmT57Mbbfdxv79++nevbVt6ccff9xhRBpg7ty5Hbo4NOHxeACwWCytzlkb/z/eNKY9hBBMnTqV3NxcrrrqqhPuFxXVduDFarW2udfMmTO59tprKS0t5f333+fYsWPU19d3uE9HSJJERkYGF154IVdeeSVJSUl88803LFy4kIaGBlatWhVONzxTGAL5LKH54/gIi0q0zUxlgw+PX6Oy3kdWYgS7i2spq/Pi8gXRTWaELtAFHLAloASDaJKEJAQi/DcYauPsttiINbmIxIezpBxZ07h+09tkVhejSxJLLriR/andqTBH4lEtCCRsAQ9BRSW5ppSgojLKV0S/77+gS9F+ZEVCjrAzfetqpAFXs9vkRA16Qdfwq2bKzRHIuoZEqEMekoQsCQKyiiygzmyjWjgQsszGcj+739/NoE7RXNNY5CbHxXLUJyFUE5KnFocIEhHloLzeS0mthz6dYpCsVkR8Ip5ABVaTjCQpCKHj8eskRreMDJ+pIriTEaBteRoXlLuYMCzzlNwWTrVD3o9pNmJgYGDwn8x1113HnDlzKCkp4Z133mmzUK2JplSK3Nxcjh49Gj5+zjnnoCgKK1as4PHHH2817+KLLz5jRXpN0V6fz9fqXJPrRkcRYQgVvW3atIkdO3ac1H5t7dW0X1t79erVK5z2MHnyZO6++24uvPBC9u/fT3x8/An3PJ7nn3+eRx55hH379oXTNMaNG0dmZibTpk1j4sSJJxT6p4qRg3yW0CTiqhr8VDT4iLSqjOqVxE3ndyLTEuTg4TI+3lZElcuPL6gT0ELiWEag6EECigJIYZ/jJjRVJaia2SRy2OtNY8jh77nluzfoXl6AjsSfz7+O77IGEeWuI8ZdS701kqCiAhJOV0goYDQAACAASURBVC3x7mosWoDk0kJ6dopFsduQHA7U9HTMGRncvfd9ZpZ9zaTib7iveiMX7/si5E6BjAi39xCoNlsouiwgqJjRFBVJlomJCOVYby6spWzWozhfXEjsksWkO61IwQBYrUg2a5v5u6oiYTMrBDSBP6AR0AR2i4KqthSETUVwM8f2Cuccn6pwbJ4jvvKrAp59fzdLPt3fytf4x3oah++tyRWjWU50R5yMt7OBgYHBL5HJkyfj8/m49tpr8fl8TJo0qd2x7733HlVVVaxevZqsrKzw16BBg9A0jddee+200iZOhdjYWKxWawuB3kTTseOL2Zrj8/mYNWsWEydORFEU8vLyyMvLo6ysLLxGXl4euq6H12prL7/fT2VlZYd7NTFp0iSqq6tZs2bNSd3j8SxcuJDzzjuvVQ7z+PHjAfj8889Pa92OMCLIZwltORn0iDXz3Yw55Ju6YfL70GLTUVUTQTn0ZyGEQNICqJqGjIZftSCaPSLqLJUQJbn5XmSj6RI2r5uRBzeQ4KpCAG8OGss3XYYAEn6zlfTaEnRZJqCYSK4rxeEPPZaRhSA1MZr4Z58jsHcvNU88hVZTiykqiuhn55GYnkb1/Q8QzMvD51P5KOcifLKKbjIDoCERF2mhyuVHArxBHRNgUmV8AY3Ixhzro26N3n1CUdKhz81hw/9uZ2uVhu4KtBn17RTnICnKGlonqGNRZfwBnU6xratlf6xn7sl2uztTnsan2iGvI2Fu+AQbGBj8kklJSSE3N5dPPvmESy+9NGxZ1hbLly8nLS2NRYsWtTq3fft25s6dy/r16xk1atRPdr2yLDNgwIA2U0C+++47gBZ5usfj8XiorKzk9ddf5/XXX291/oYbbgCgurqamJiYsEdxYWEhGRkZ4XEbN25E1/UO92qiKbJdXV19wrFtcezYMXJyclodb2rQEggETmvdjjAE8lnE8SIukJdHcbUbPVHCp4bEpj3oo8Gs/lAAB0R6G7D7XZiDQfYndQm1cSYUXZbRkYSOOeBjxuevkOAKdbT5oNfF7EjNQRICXdfQgxpV9hjMQR89j+3naEwK1XYLMjC4azyDR5+P8PmofeoZgrt3I0dGEpRlJIsZyWIh8u7p6A0NdJ96Jxce+JrNnfoRtDnwC4GumHDao6h2BTCpEkFdIIRACLCalDZFpGKzMX3CuR1an+WkRDGkSxybDobuKRAUnJMdd8qpEydTDHeyAvRMehqfSk600WzEwMDg18zcuXMZPnw4l156abtjysvL+eijj5gyZQrjxo1rdX706NE8/fTTrFix4rQE8qnYvP3+979nxowZvP/++1x++eUAuN1uXn75Zfr06dPC1eH4dR0OB2+99VarNdevX8/ixYt5+OGH6du3b9g6beLEiTz99NO8+OKLPPfcc+HxL774ImazucVrUVZWRmJiYqu1mwrphg4degqvyA/k5OTw5ZdfcujQITp37hw+vnLlSgCGDBlyWut2hCGQz2LU9HTSnHZkBKZgKPqKoiDLgC7QAYGEz2Rm2OGtWFWJvIRMFEUjIJk5SBroOqZggAc/+zM5pXkA/KvHCP6vxwh0JFQtiE+1cDQqMeRd7PfgjLDy202rKY+MJ8lTQ/Y3ldR9FE+gpAQak/j1+nrUnj1REhLCxWSyMwYlPo4/UMjIvKOU2p1ocYm8Z8/CrjuJtpuodvkRAiyqgqrIePwavoDepog8UdT3TPgHn2wx3MkK0J/L09hoNmJgYPBrZvjw4QwfPrzDMatWrSIQCHDllVe2ed7hcHDJJZewevVqFi9e3ELkrlq1qs0ivaFDh4aL+hYtWsQjjzzCunXruOiiizq8lttuu41ly5YxadIk/vu//5vExERefvllCgsL+eijj1qMPX5dk8nE1Vdf3WrNhoYGAEaOHMkll1wSPj5w4EBuvvlmFixYQH19fbiT3ptvvsmcOXNapFiMGTOG5ORkhg0bRlpaGuXl5axZs4YNGzYwYcKEVvfVlK/d1C76888/Dx+77rrrwtZyDz30EBMnTmTYsGFMnTqV5ORkvv76a1auXEnv3r255pprOny9TgdDIJ8FHF/81T05kv3H6imucpN0z10MXr+TbS4Vs0/gDgqELjBpARyeBqK89fgVlcL4DPITMumslpMplfK16INPmFCDQWZ9upi+xXsAWD5sIt92OYd0rYFRNQfw7DrEu30vw6z5scpg87nYaUkgIyYFkx5ETk5CfF+ASIiHqqqwVZvauzdxf16MVl7+QzFZaSlKfDyipoaeKSnkSH72lh8mNiuDYz5wWFRMikx6nI3rR3ZBRqKk1nPaIlJ4vWhFRfRMTz/tNIKTLYY7FQH6c7RANpqNGBgYnO2sWLGCqKioDsXrlVdeyfvvv8/bb7/NtddeGz7elLZwPH/605/adL04ETabjc8++4yZM2fywgsv4Ha7GTBgAB9++GELcXumeOmll8jIyOCVV17h1VdfpXPnzrzwwgtMnz69xbhbbrmFt956i8WLF1NVVYXD4aBPnz689NJL3HLLLa3WnT17douf161bx7p16wAYMWJEWCBPmDCBzz//nCeffJK//OUvlJeXk5KSwh133MGjjz4adu84k0g/dTL5fwpDhgwRmzZt+rkv499Oa1uwkNjRddB1DXHkCH0qChih1lB7xQS+d6l8va+MuPIi7LWVAOxNzMZrtqJLMg7JS5pUzj69E6oW5IFPlzCoaCcAL5/7ez7sk4sqdGRd58J0G/23fMY/InsQ56pGaBoCyIvNwC6DraEaWQj6HDvAtd+/j+x2oSNxMD6T8oHDyXnwbroGqml4/oVwu2fns8+glZcjp6WxZF0Bmw6UoqsqAV0iPdbOjRd0OSONME7HBu3HrhP+IGMIUAMDAwMDg38Xbb7RGhHkXzHC62Xntjw25dcSG2lFkiTqPH72l9TTIyWSCF3gd9eyIzaTYZ+vZ9CejTikKHYOGIetoRYAl9mGRzUTQwNVUgwN2NmnZ6BqQe7/10thcfzq0Al82OcSJKGjIyELne15ZZxzzX9h3nIU/XADHsVMrTkCj8lKSt1RItx1uCwOvskcSKdgHSM3f8KqIePYmZqDLqsoL6+jX/VhbnC5iHniMcw9eyJZrcjR0ewurmXLkVri4qLCKQkV9T5kWTojovJUbdDa41SK4X6OyLCBgYGBgYFBawybt18pTZHL/YteJlB4BKmxPbLPH0QIHZ9fQ7JZkS0WNLeHUlMkWlk5XQ5so/fhHdSaHVTZY6iwO0lXqjhX3UuMqANdR9GD3PfZUoYc+R6AFedczXt9fxPat7ENdFBWUYN+/CtXMKhrAgejUsl3plMaEYsmy9RYoyiKTqYwJpUaWyRv9RzNkgtvZEdqDtGeOmK9tUS7a9luS2bvwTJqH3kM0cyH8UzZnbXHqdqgdcTxLaINDAwMDAwM/rMxIsi/UpoioGlpPZACfnS3B9luQzl8CNRopMIC6rOzcSd1wl9YTEJDBdTWIgPXfreag4mdOejsxOqBl3NEikPTZRo0CypB7lm/jKGF2wB4bfBVvNt3dLOdpRbfJVUfI+HNpfwrIxezrGGKdFDt8lNjjUToAlnoyLJMfFQEe/UclIAfyduAHBkJsozuE5Q6Yum6YyuVU6eF85JTHVEdFrX92K5vp2qDZmBgYGBgYPDrwRDIv1KaIqBZRfvoH9WZXbqCVlGP5vbSSXdR4ojF29jkwWax803nwXQtLUCWQr7Eka46vh4znBpzNAgIuBSS5HomffUGww5vBeDvg67gvb6jkRAIAF0DWQYhkIROckMFmQW7eGbIf1Gj2lAREJBAkvFLCkKRkCUJWQ9SUVWHEgyEWlDLEphUTDk5yHvySXJVIkdFoVVUUHXHNPTqGhLT0xk09g42HaxEmEzIshIuatN1weKP9rDpQGnonCQxMFbhziv6ozR2/DkZAX0mWkM352Ts3gwMDAwMDAx+fgyB/CulKQIayM9nuoB8ewKF+w5jX/0PNAGLh/6eRIcTe5QD094j7ErJ4WBSF7qW5lOS0InHf3sHA8z5VIoY9DqNnPKD/H7LeyTWlQPwQe9cNmb0p1NNCUEkip2pCElqtE5uTLMQ8HaX8zjiTEPVgqhCQ9KDeFQzsiQjyxIWRSC7/dSZ7MT6vPQoP0hxXDpCM2Gq9zHsgv7kFKdAlQU5Nha9qgolMQmtsJCJf5/HoICZspQsut9zGz07JyDLErsOlrHhi+1Eu2uRLRZ0YEO+wobP32HY/LkIs+Xf3jb5TBX9GRgYGBgYGPz0GAL5V079C39CKyoiOT2dns8+Q3X3VNYWeRCSjFZRjohyYO7bB7HnIOXdemMP+njoNzOoNUXxlWbHWVuDEvQz7ODmsDiWOmdS6UxCFTo6MpqqoAidgKQgi8ZosiSxM6UHhbHpNFjsmBAEkUGS0ZGwqBIxERZq3QGCioomIKWhgjt3/i8F5mgqBwyjx9hL6dk5AWn0MoKNBXPV9z+AVlQEcbEc8JooS+pEUkkBXbVaZDlkTl6YX4wWCCKbzQiPG0kAEbEcrfURLCrigC3hpLrWnUnOVNGfgYGBgYGBwU+PIZB/xRwvyrTycuzT72Trnz+lIiIWVdeRSuuJtpuJSE7C3Pt3rOjeDUmWkISO6teocibyh3WvcE7hdgAsY8ZgG///uPaueziYmk2psFISm8qnXc+jzhKBIgkEEl7JhAzYND8+YUNYrCT7apC8PhrMdqxxDtLiHLj8Gl6fFa/by+/LSlD9Xrr6PPQJlhKbGoUkS9As1SH2xYX4jxzhL3s9bPh6F1ogiNI1h6EHAkzLFsiyREZ2GoppL7rbhWyzh7oCBgOkRltQ09MpPlD9b2+bHC76a4wg/5iiPwMDAwMDA4OfFsPF4lfM8U4MSkIC2/76d4qsMcS4a0GSEEBFtYvIggO8dMCDVfERq9UR76rCbzJzy9d/54L8UL/3z3pdSOkfH8d2/nmo0VF0PZrHSFchF97xX9iSE1EUCZPQQznEsowsdKK89UT5XeiShDc2AUmWOL98L0OO7KC6wYc3oCHLChf068Q5d92IkpqC5ZwhaKWlBIuKWt2TZLWSb09ky1EXCT2zSe7emYSe2WwurGVvY051z84JDB3Zn/ouPajp3I2GrO4MPac7Q5+bg2S1kupQkHw+hBbq4f7vaJvclPLifHGhkV5hYGBgcAZZv349kiSFv2RZxul0Mnr0aNauXdvh+Hnz5rW55vz588Nj1q9f3+Lchg0buOqqq+jcuTMWi4Xk5GTOO+88HnzwwXA3OoBXX321xXUd//XQQw+d9j0Hg0Eee+wxsrKysFqt5OTksGjRIk6lt4WmaSxatIiBAwdit9txOp2cf/75fPzxx+3O8fl8dO/evc3r9/l83HrrrQwYMACn04nNZqNHjx7ce++9lJWVtRhbXFzM448/zvnnn09CQgLR0dEMGTKEpUuXomlaq32XLl3KpEmT6N69O7Ist2g3/VNhRJB/xRzvxBAsKqK4PgDpDjJc1fg6p+LTobqhmh3R6fhlEztNA3j2kkT+tqOGAf94mZF7Pgdg3YDRfHLZjSR7dHpGRlG56h0Ob9lD5qCe9OmawkgO8M9txbh8AQQSsi6IDnqJMCs4/LWY7YlcULCBvhs/pWugBiUpkbIJoyiNiAs3xZD8PtTsrmhFRUjp6eyXozi6o6RVEV3Y4k1RwRH6E9YJhiPAsiwxbUxP9vZPa9V0Q3i9JD79ML1FBt/Hd0HulN6iwO+n/n0YaRUGBgYGPw1/+MMfuOiii9A0jYKCApYuXcqYMWP4+OOP2+wuZ7VaWblyJTNnzmx1bsWKFVitVrxeb4vjb7/9NhMmTCArK4ubbrqJtLQ0SkpK2Lx5MwsWLOC2224jIiKixZxZs2bRu3fvVnv07dv3tO916tSpLFu2jClTpoRbP0+fPp2qqioefvjhE87XdZ2rr76af/7zn9xwww1MmzYNt9vN7t27OXLkSLvznnnmGY4ePdrmOZ/Px7Zt27j44ovJysrCZrOxa9culi1bxttvv822bduIiYkB4L333uPxxx/n8ssvZ+LEiZjNZj744ANuv/12PvvsM954440Waz/11FNUV1czaNAgamtrT+GVOn0Mgfwrp7koUxISSDXrSIEAkt1BVEwENQ0euliLqSGCimAii268gM6pTqb9/T6se/4PgG+7DuXb3iNRFIWUGFuzAjc7yvpCBuXXcktXlVHd+rCpqB7dH6CgoISDOxqo1FVkReK8JDPjP/0cAtXo9fWovXLoO6Ar/ZpHUhsFvf/IEf5yIMCWtfltFtGlxdo7tHiD9ptuBIuKEEVF3JTgY39eHvW5d5HRM8voWmdgYGDwC2fYsGEt2jtPmDCBPn368Pzzz7cpkMeOHctbb73Ftm3bGDBgQPj4jh07+P7775k4cSJvvvlmizmzZ88mLS2NLVu2EBXVMqhSWVmJw+FotU9ubu4Zbf+8fft2li1bxowZM1iwYAEQavE8YcIEnnzySaZMmUJKSkqHayxatIj333+fzz77jJEjR57UvgUFBTz11FPMmTOHBx98sNX5qKgoNmzY0Or4+eefz4QJE3jjjTe47bbbALjgggs4fPgwSUlJ4XFTp07l+uuvZ+XKlTzwwAMMGjQofG79+vVkZmYiSRIjRoygqI0nzGcaI8XiLEF4vVTf/wBZRw/Qz19BfUZXDlV62H3MTS0OZHMUS6ddSOdUJ7X/30NY33kLgG+yBvOPc8ZRrSkMjFVAECpwsynEyxpOi8R3X3zPlv/vaVKefpjr+8dz5ZsLueN/nuDWTW9xtWs/U4u+5PaBcZgzMpCTkjAPHUrsksVtphmEUyiO1OJ0mEmItOJ0mMNFdAA5KVEMzoqlqsFPeb2Xqgb/SUWAhdeL8PlRUlMQ5eXkOM1cOiLnjLSmNjAwMDD4z6J3797Ex8eTl5fX5vnc3FxSUlJYuXJli+PLly8nNTWV3NzcVnPy8/MZNGhQK3EMEBcXh/U00+fcbjd79+6loqLihGOboqt33313i+N33303Pp+Pd955p8P5uq4zf/58rrzySkaOHImu6y1SQ9pj+vTpnHvuuVxzzTUnHNuczMxMAGpqasLHevXq1UIcNzFhwgQAdu/e3eJ4586dw3VD/y6MCPJZQlPBnjkpievz1/HBhcN5bWcNAhOuiG786fpBJCVEU/foY7iWrwBAWKwk1FUwbsM7JCtBzpu9nHVHPGiaRnDXPoTXCyYTujBTGp8OFUXUfLiRmGo/3VJT6bZ5CzmHg6hdu2Lpmo3luMYb7XkRd9Qlr0nM3nFJ99Dc41Io2qO5zZqckkLMs89gys4+o7nAP7Y5iYGBgYHBmaOmpobq6mq6devW5nlFUZg0aRKvv/468+bNQ1EUNE1j1apVTJ48GVluHUPMysriyy+/ZM+ePfTs2fOkrqOurq5N4RsdHY3JZAJCec2jRo1izpw5zJ07t8P1Nm3aRFJSUlh4NjF06FBkWWbz5s0dzt+7dy+FhYXceuutTJ06leXLl+PxeMjIyOChhx5iypQpreasWbOGjz76iK1bt57gbiEQCFBbW4vf72fv3r088MADSJLE6NGjTzi3KX0jISHhhGN/agyBfJbQ3EXhy67n8v3ObxmERlEgk0c/X426z0ltWiquvy4DQE5KQq+spGtNEV29FahpqYiKCtJiE5ACAXSPB0lVEX4/ks3KBimW4q49kSotiK6j6VtRwE3nWYieMQNTdpewEDV17YrwevEdOBBKozhSG06jGNQpmlu7mUh1RJ92CkV7tHD0KClBsljOuDj+d3srGxgYGBj8QENDAxUVFei6TkFBAbNnz0bTtA4jntdffz3z589n7dq1jBkzhk8//ZSSkhKuu+66NtMFZs+ezeTJk+nbty9Dhw5lxIgRnHvuuVxyySVER7f9fjR+/Pg2j69bt46LLrrolO/z6NGjpKWltTpuNpuJi4ujuLi4w/n79+8HYOHChURERPDCCy8QGRnJX/7yF2699VY0TeP2228Pj3e73dxzzz1MmzaNvn37cujQoQ7X/+qrrxg1alT458zMTF5//fUWKRNt4fF4eO6550hNTT2t1+VMYwjks4Smgr0163Yz/9tynKKOTjaNxzf9D87YKAKbNuH7MFRlarvqKvSGBnzffANeLygKanY2ano6OWYLQ7rE8e1BM7pfICtmuvXrxsGyZOKtCnKEFRGVza7qZMrG3kJCl8QW19EUyd1b7WdD19Ek9MxGUlSEFuS7L75n0Cuf0N1pZtC4u9hcWItOSBz/2CK6n9pmbc+hcjbuOUqc0xG6nzPkrWxEpQ0MDAxOjhkzZjBjxozwzzabjUcffZTp06e3O6dfv37069ePlStXMmbMGFasWEH//v3p169fmwJ50qRJJCUlsXDhQtatW8c333wT3uuPf/xjm84U8+bNY+DAga2O9+/fP/z9RRdddNIOFB6Pp80UDwgVHno8ng7nN6VT1NbWsnHjxrAjRFPO9sMPP8yUKVNQFAWARx99FK/Xy6OPPnpS19e/f3/Wrl2L2+1m69atrFmzhurq6hPOu/XWW8nLy+Pdd9/FYrGc1F4/JYZAPotY9V0B//PtfgROEv0BHtj8JraSYvzFOqKsSRyPI2L6NGpm3Idl6DloRcVEzrwf26iLkKxWJODWHBsDX/2SsuhEEmvLqLu0Hwd2lhN014SKAvv0RlgsHHVrHF+32xTJLe08GC0QBK8v5ETh9aEHApTFp9Pt8BZu7WYiv3+vk06hOBHHO3qcyeix8HrZt3ApAXNngkc1TH16I8nKj/ZWNqLSBgYGBifPvffey2WXXYbX62X9+vU8//zzNDQ0nDB39brrrmPOnDmUlJTwzjvvnFAI5ubmkpubi6Zp7Nixg08++YQFCxYwe/ZsMjIyuP7661uMHzhw4Bkt0rPZbPh8vjbPeb1ebDbbCedDqHiuuV2aoihcc801zJ07l927d9O3b1/27NnDggULeOmll9qNkB+P0+kM3+8VV1zB7373O84991wsFgt/+MMf2pxz//3389prr/HMM89wxRVXnNQ+PzVGkd4vEOH1EsjLC+UAnyQrvjjI+nXr6C/lMShB4ZEdbxEd5wS9mTi+YizOhc9jysxESU9Hr6xC7dYtLI6bMHfqRM84CyOO7aJnnIW0SBUp4AezGeH1ors97foKN0Vyk8qPoJhUsDZ+SrRakE0mEitCEd6mPS6yuugZZzkjgrDJ0eNMexAHi4pIKilAURV0rxfh8Z4Rb+W9JXXhjn9tFSsaGBgYGPxAz549ueSSS7j88st57rnnmDt3LvPmzeO9997rcN7kyZPx+Xxce+21+Hw+Jk2adFL7KYrCgAEDmDlzJl988QWSJLF8+fIzcSsdkpqa2qbVmt/vp7KyktTU1BPOB0hOTm51rqlwriniO3PmTLKyshg5ciR5eXnk5eVx+PDh8Ji8vDxcLleH+w0ZMoTu3bvzt7/9rc3zjzzyCM899xwPPvhgm5Z7PxdGBPkXRvNiMyU9vVXTCeH1toiSCiH467o8/vZ/B1HoTFSnHsz//TDc36/Gv3kzeqM4tv72tzhffAFJVUFViXnheXZu2ktxjZe0jXvoMyQHpfFTZ/NorJKQgONIEf31arbJTrDbMekKQ7LbTolomjvkyBGGHgg0plEEkZE4d2Q/htw8GHOnTgAd3ud/Emp6Ot2dZvpWHgp5Kwcl5JN01uiIExUrGhgYGBi0z/3338/SpUuZOXMmv/vd79osugNISUkhNzeXTz75hEsvvfSEFmlt0a1bN5xO5wnzf88EgwcPZu3atRQWFpKRkRE+vnHjRnRdZ/DgwR3O79u3LxaLpU2rtKZjTUVyR44cYf/+/XTv3r3V2CVLlrBkyRLWrFnDuHHjOtzT6/W2mWbR9EHmjjvu4Mknn+xwjX83hkD+hXF8++jmTUCUhASq738gLCqdLzzPC5/s5vutm5DIYETPFB67uj9mVSbQuxfeDz4AwDrmUmKXLEJqrKbVdcGSdQVs+GIvQZcbWegMePtz7n3y1hYiWU1PD4vYG1JS+M2Nd1DmiCMtKbrDlAjJasXSrRvTskW7ThSBvLxW9/mf2mRDslqJf3Ehdx85Qp4SzVG3dkbSQk7G79nAwMDAoG2sViv33nsv9913H6tXr2bixIntjp07dy7Dhw/n0ksv7XDNjz/+uM0x33zzDVVVVVxwwQWnda1ut5vCwkLi4+OJj4/vcOzEiRN5+umnefHFF3nuuefCx1988UXMZnMLsdrWuhEREYwdO5a3336bnTt30qdPHyCU2/zaa6+RmZlJTk4OEBKwze3ZAMrLy7njjjsYP34811xzDUOHDgWgoqKC2NjYVh9EPvroIw4dOsQNN9zQ4vif//xn7r//fq677joWLVp0Ki/XvwVDIP/COL7YTElICInUI4VgsSI8btTkFAJFRcx7exub9ufRQ6qgU/c+PDyhP6oiU7/0L9TPexYA87BhREy/EzQNGgXy3pI6Nh0oJbqhCrw+hIBtUbHs2p5P3wFdW4jy5s4QvVIi6d81o6PLb0FHThQ/dVHdmaZJ9PeGVnnXp0uT3/Omg1VnrFjRwMDA4Gzitttu44knnuCJJ55gwoQJ7eYjDx8+nOHDh59wvfHjx5OamsrYsWPp0aMHQgh27NjBihUrsNvtzJ49u9Wcf/3rXxw7dqzV8czMzHCTjlOxeRs4cCA333wzCxYsoL6+PtxJ780332TOnDktUizaW/epp57iX//6F6NGjeKuu+4iMjKSV199lSNHjrB69erw69RW7nSTi0VOTg5XX311+Phrr73G4sWLGTduHF26dCEYDLJ582ZWrVpFYmIic+bMCY999913mTZtWjh6//rrr7fYo6l4son33nuP7du3A6Godl1dHY8//jgQKgocO3Zsh6/Z6WAI5F8YbbWP1o4Uoh0rRa+vR3I40CWFxX2v4NP9dUAiA/r24sGrBiH7fdQ++wINw9FkigAAIABJREFUfwp9UpMTEvDv3k3lpGuxnDec2EV/QrJaKa5yI0wmZJsd3etDkkKeyJvqZPJmPktSSQHdnWbinn3mJxOxJ1tUd3xKyclyuvP+nZyO37OBgYGBwQ84HA6mT5/OI488wnvvvfejC8D+9re/8f777/PBBx/w17/+Fa/XS0pKCuPGjWPWrFn06tWr1Zynn366zbXGjx9/0l3sjuell14iIyODV155hVdffZXOnTvzwgsvdOjY0ZyuXbvy1VdfMWvWLObPn4/P52PgwIF8+OGHJ4yit8eIESP47rvv+Mc//sGxY8fQdZ3MzExuv/12Zs2a1UK4b926FSEEJSUl3Hjjja3WmjNnTguB/I9//KNVfnfTh5EbbrjhJxHI0snaivzSGTJkiNi0adPPfRlnHOH1UvmHW/B9twE5MhItMYkll92Kr+YA20Q3Lhvag3svy0Hy+yi7YhzBXbsAMA0ZjF5bh37sGEgSSmoKsUtfwtS1K7uLa5n3vztxEkASAl2S2F+nE6EK1JKjyJJEv5rD3D1zIuZOnX6U0PwxQvVE+dhnep6BgYGBgYHBr442o06Gi8UvHMlqJXbJYsxDhxJMTObZPv+P76pBAFf8/+3dd3hUVfrA8e+ZFFKBkALEUEKvgpIVBDV0hBWp0kJdVFCKgIsgCqirsqj8QJqgiKIo1ZUFBaWDK4JSlGYIAaQTUigJ6TPn98dkhplk0iAwCbyf55lnyLnn3vvmErhvzpz7nodDeLlTHQwGReL8+dbkWPn4UGbqFFyrVweDAZTCNTTUOgJcy1tT7+huLkefIeZsDJcyzH0eKOdFucQESidc4g/PChw3ed1WZQhLonpl9BgSRo8pVFUOcDwf+07uJ4QQQoj7g0yxuAcYypTBe/Zspk7/D/u9zBUg6rXozNC2tVFKcePrZSTOmAmYk2P3xx7DvV49/OfNIePESUBbl13WqalcHTGSvnt+o1ml2lyuGMrVv0WwIyYDlZaONhmt1RTOnrxIg9o5V/MpKEeJamEexLvVecolbX6zEEIIIe4uSZCdpChXSLuRmskrX+2mnPclQnQp2p2JYlBodXNyvGIlV1+ZAIB700co8+YbuGWtS5957pzdMtCWNlNCAq6+PlQ/G0ndiqW53LgqP286gTZpFKCVwqAh2Mfltq7B7Saqt7r4x51cNEQIIYQQJZ8kyE5QlCukXUtOZ+zS/fyZYKC2CuTJs0fpaohDp6VxY9lyro5/BbTGPSwM/y+/wODtneccXNeQEFwqVQYNbvXqUm7+PPx9S9Mk9Ap7T8SRERAMaak05hoNwurc1nUoikTVMsXjbu0nhBBCiHufJMhOYLtCmqW+rWWFtMIsAJGQlMYri7cSFW9CKVd6dupAe+9kEmfOJOHZ5zGeOQOA8vJC+fujstZVz2tqQ25J64ttaxFZvwLnYypRPv069RtVt9ZEvh2SqAohhBCiuJEE2QmKYoW02OupjP7sF6pf2YfBUI5nenSjXYOKZERHk3HkqDU5xsMD90cewXTpEulnz3LCK4hz193xDW1A6KnDuDmY2uAoac2rZrEQQgghxL1EEmQnuN0V0i5eTWHUkt84l5BOiqEmI7s0pW0D89KYGYcOYTx7FgBVpjRuYX/DFBeHCgnh4+MZ7D97FKPWGBp05qEnujLy6UYyB1cIIYQQwoaUeXMCywppCUnpxCamkpCUXuAV0s7E32DCx+tJTrhEKTcDEyPa0LZxVQBSNmzgyphxoDWuNWtSfttWAj5egN/sWVye+Bb7z17Dz9udQG83/AxGDiQYOZaQfoe/WyHufTo1lYzo6EKXKhTiXrB9+3aUUtaXwWCgXLlydOjQge3btzs7PKcZPHiw3XVxc3OjSpUqPP/88w5X1iusGzduMG7cOIKDg/Hw8OChhx5i+fLlBd4/JSWFCRMmEBoaSqlSpahatSqvv/46qbn8P7Zy5UoeeughPDw8CA4OZsyYMSQlJdn1eeONN+y+5+yv5557zq5/bv2yr+AXFRXFpEmTCAsLw8/PD39/f1q0aMHKlSsL/P0WlowgO8GtrpB28nISoz7/lTrJJynn4sbAiDY8HOoPQMrGjSQMfxEyM3GrVw//FctxKecHgFuNGpzff4bMlFSUh4GMo3+ab+heZTkfU+mWp004WuSjINU5irKChxDOJgvPCGE2dOhQWrZsidFoJDo6mgULFtC2bVs2btxI69atnR2e0yxZsgSDwUBycjK7du3i008/ZceOHRw8eJBSpUrd8nG7d+/O1q1bGTNmDLVq1WLFihX07duX9PR0Bg4cmOe+RqORjh078tNPPzF06FCaNGnCgQMHmDZtGocPH2bNmjV2/b/66iv69+9P69atmTNnDsePH2fWrFkcPnyYTZs2WaeMdu/enRoOniv66quv+OGHH+jYsWOObc2bN+eFF16wa6tYsaLd14sXL2bevHl069aNf/zjHxiNRlasWEHv3r3Zt28f06dPL9A1KxSt9X3xatKkiS7JIi9c0+3/vUU3nfKD7vjOer3/xCXrtpRNm/W5KqH6XHCIvtSmrc6Mj7fb15SSone9OEFHvLxEjxi3SI8cNV+P/Oenut/LS/QfvxzK9ZxGo0kfOXdVbzx4QR85d1UbjSa7Y8Y9N0zHdPy7jntumDalpGij0aRnf39ED5y1VQ+Y95MevGCXnvNjpN1+RqNJz/kxUg9esEsP+Ohnh32EKEnSjx83/zsYOFjHdPy7Tj9+3NkhCXFXbdu2TQP6k08+sWvfv3+/BvSTTz5512O6cePGXT9ndoMGDdKAzsjIsGsfP368BvTKlStv+dhr1qzRgP7www+tbUajUTdt2lQHBQXp1NTUPPdftWqVBvS0adPs2mfMmKEBvWHDBmtbWlqaLl++vA4LC9OZmZnW9rlz52pA/+c//8k33ho1amh/f3+dlpZm1w7oiIiIfPffu3evvnbtml2b0WjU4eHh2sXFRV++fDnfY+TBYd5YIqZYKKWaKKVmKaUOKqUSlVKXlFJblFJt89+75Dt89ipvfvo9wSnHKevpyqwhLXioWnkAUrdvJ/655yEjA9datQhYvgyXcuXs9s88d47QU4dplJnAFdxI8CxDgnalsb5G/UbVHZ7TUoru/e+O8uXPp3j/u6PM3xyFyaStx8xeCePPv2L59ac/8D15jLKno/HzdLFW57CwreAR6OuBn7d7jj5ClCTWet6xsbLwjBA2HnroIfz9/Tlx4oRd+44dO2jfvj1lypTB09OTZs2a8d1339n1SUhIYMKECTz00EPWfmFhYXz99dc5zmOZynD27Fn69OmDn58f9evXB8zTECZMmED16tXx8PAgMDCQ5s2b5/ho/vz58wwePJjy5ctTqlQp6tWrx8yZMzHncDe1bNmSkJAQzpw5Q9euXfH19aVcuXIMHz6ctLS0Al2X8PBwAKKjo+3ar127RmRkJNeuXcv3GCtWrMDT09NuyoLBYGDkyJFcvnyZrVu35rn/zp07ARgwYIBdu+Xrr776ytq2fft2YmJiGDlyJC4uN9c/ePbZZ/H29s53WsfPP/9MdHQ0ffv2xd3d3WGf9PR0bty4kesxmjRpQunS9tNQDQYDPXr0wGg0cuzYsTxjuBUlIkEGxgMRwC7gZeA9IAjYpJR6Ia8dS7r9fyUw+ou9uGUm4ueSytyBTaiVNVc5dedPxP/jWUhPx7V6dQJWLMMlICDHMVxDQnALCWFA1GZGph5lUI+mTOj5MOPefjbXUm35JbKOkoIzJ85jzMjE4O5unouZmmatzmGRVwUPIUoiS2lEv9mzZHqFEDbi4+O5cuUKATb3pW+++YY2bdqQkpLC1KlTmT59Okopnn76aVasWGHtd/LkSb7++mvCw8OZNm0a7777Lp6enkRERLBkyRKH5+vYsSNGo5F3332Xl156CYAXX3yRmTNn8tRTTzF37lxeffVVatasyS+//GIXZ/PmzVm2bBkRERHMmDGDypUrM27cOEaNGpXjPCkpKbRp04aAgADef/99OnfuzMKFC3n77bcLdF1OnToFgL+/v137t99+S926dfn222/zPcbevXt58MEH8cx2D2/WrBkA+/bty3P/9HTz80deXvbFAby9va3Htz0XQNOmTe36lipVisaNG+d7Lsvf1+DBgx1u//bbb/H09MTHx4eQkBDeeustMjMz8zymxYULFwAIDAwsUP/CKClzkGcDg7XW1pnjSqmPgN+Bd5RSn2itC3Y1S5Dd0XG8+vVeUoyKa6Vr8eaAh6kaZE6O0/73M/FDhkBaGq7VqhGwagUuQUEOj2Nb27h8ARfkyK8UnaN6yZWrP4CLWySm5BsYPDzAoxSGZKNddY7breAhRHEk9byFgKSkJOLi4jCZTERHR/Pqq69iMpno27cvAMnJyQwbNoyuXbuyevVq634jRoygWbNmjB8/nl69eqGUomHDhvz11192I5Zjxoyhbdu2TJs2jUGDBuU4/2OPPcaCBQvs2tauXctzzz3Hhx9+mGvc06dP58yZM6xevZoePXpYY+rRowfz5s1j2LBhNGzY0No/ISGByZMnM2bMGACGDx/OlStXWLhwIf/6179yHD8+Ph4XFxeSk5P55ZdfePPNN/Hy8uLpp58uyGV16MKFC3YxWQQHBwPmEfG81K5dGzCP5nft2tXavm3bthz7W5LQBx54wOH58kqQU1NTWblyJQ0aNKBJkyY5tjdt2pRevXpRvXp14uPjWb58OVOnTuXQoUOsWrUqz+/h8uXLfPzxxzRp0sT6/RSlEjGCrLXeZZscZ7WlAN8BfkAFpwR2B+2MvMyHX63nb6Y/qFrWlQVDm95Mjn/5hfjBQyA1DZeqVQhYuRyX8uXt9jeZNEfPX2PToYscPX8N7V4Ktxo1Cjy6ZZvIAg4TWUtSYDlm3aqBPPJ4IxKr1eZqlRokJBtzVOe4nQoexZlUMRBC3O/Gjh1LYGAg5cuXp0WLFuzbt4/XXnuNkSNHArB582bi4+Pp378/cXFx1teVK1fo2LEjZ8+eJSoqCjCPTlqS4/T0dBISEoiPj6dt27YcO3aMxMTEHOd/8cUXc7SVKVOGPXv2cDar/Kkja9eupUaNGtbkGMyDQuPHjwdg3bp1dv0NBgPDhg2za2vVqhWxsbEO46pQoQKBgYFUqVKFPn36EBgYyA8//ECFCvapy+DBg9Fa5zrSaislJcXhA34eWffjlJSUPPePiIigbNmyjBgxgm+//ZbTp0/z3//+l2HDhuHm5kZy8s1PdS3Hyu18qampOaaiWKxZs4Zr1645/IUGYPfu3YwbN44uXbrwj3/8g40bNxIREcHq1avzrICSkZFBnz59SEpKYuHChXl+r7eqpIwg5yYYyASuODuQorTp0EXe+M8hfE1eVPT048Mhj1K+rPljlLRffyV+4GB0SgoulSsTsHIlLtme9iyKpawtiezekwmYMCfHeSWyOjUV47lzvNgqlGONHsi1OsetVvAobmwrcQR7uxD07yloqWIghLiPjRs3jo4dO2IwGChTpgz169e3JmwAkZGRAHTr1i3XY1y+fJnatWujtebDDz9kwYIFREVF5UjArly5gq+vr11baGhojuO9//77DBo0iCpVqtC4cWPatGlD7969CQsLs/b566+/aNeuXY5969WrB9ycEmERFBSUY2qDn5+5alRCQkKOuH788UcMBgOxsbHMnz+fw4cP4+p6e+mXp6enwznPlhJt2ePLLigoiO+//56BAwfSvXt3AFxdXRk/fjybN2/m+PHjducCSEtLyzGHODU1FQ8PD+unzdl98cUXuLi4EBERUeDvbcKECXz11Vds3LiRli1b5thuMpkYMGAAO3bsYOnSpQ5HpotCiU2QlVL1gO7AWq117jO7S5jvDpznw//+ilF7EVQhmMkDn8bP2/wDmbZ3H/H9B6KTk3EJCSFg1QpcHwjOcYyiWMq6MIls9jJXdWfPyvM8JX1Vvuy/gKi0NOrrygwJTMuxdLcQQtwv6tatm6N+rS2TyQTARx995LAUGECDBg0A+OCDD3jllVeIiIjgtddeIzAwEFdXV9avX8/MmTOtx7LlKCl85plnCA8PZ926dWzevJnFixczY8YM3njjDaZMmWLtl1uC52ib7bSP7ByNpLZu3dqaEHfr1o2wsDB69+5NZGRkjjnABRUcHGyd+mDL0maZapGX5s2bExUVxdGjR7ly5Qp16tQhMDCQxYsXU6tWLbtzWY6dfSrDhQsXcj3XpUuX2LhxIx06dMhRti0vVapUASAuLi7HNq01zz77LCtWrGDBggXW6Tt3QolMkJVSpYFVQDIwNo9+zwPPA1SuXPnuBHcbVu85w7L1O2mhThAT9AjTB/+N0p5uAKTvP0B8RH/0jRu4BAebk+NcnpYvzFLWjmoZWxQ0kXVU0eJeThBz/ALi5crBgGpERUdTpwirGOT1dyOEECWNJSkuV65cnok0YH1Ab+nSpXbtW7ZsKfR5g4KCGDp0KEOHDiU5OZlOnTrx1ltvMX78eDw9PalatSp//vlnjv0sbVWrVi30OXPj4eHBv//9bzp37sysWbOYNGnSLR2nSZMmrF271jqCa7Fnzx7r9oIwGAzWX0oADh48SExMjF11DMux9uzZY5cgp6Wl8fvvv9OpUyeHx166dClGozHX6RW5sVT3CHLwXNWoUaP47LPPmD59eo5pLkWtRMxBtqWU8gTWAdWArlrrM7n11Vp/rLUO01qH3YknHIvS0v+d4oP1f3KJciSWrc17/2hzMzn+4w/iIvqjk5IwVKhgTo7zSPgLMn8Ybo78Xhk9hoTRY255/uz9VuYqxy8gLq4YKoWQOGx0kU2vKKq/GyGEKC46dOiAn58f7777rsM5spcvX7b+2cXFJccocWxsLJ9++mmBz2c0GnOUTPPy8qJOnTp22zp37kx0dLRd9QitNR988IF1e1F66qmnePDBB5k5c6ZdabPClHnr3bs3ycnJLFq0yNpmMpmYN28eAQEBdguzFPS4mZmZvPzyy/j6+jJ8+HBre8uWLQkKCmLevHl2fyeLFy/mxo0b9O7d2+HxlixZQtmyZenSpYvD7QkJCTnajEYjb775JkCOxHvChAnMmzeP1157jVdeeSXP76UolKgRZKWUO/At8CjQXWu9w8kh3TatNYu2RfPjjt0oAgirHsQ7fTrg4Z71cMLhw8T1i0Bfv46hQnkCV63ENZ/fZgs6f7goRn5NJk1kfBrnhk+iQvp16jeqfs+PdjqsxGFwoXLd0CL73u+3UXkhxL3P19eXRYsW0bt3b+rXr8+AAQOoVKkSFy9eZPfu3URGRlprJnfr1o3XX3+dPn360Lp1ay5evMjChQupVKkSsbGxBTpfYmIiDzzwAN26daNRo0aUK1eOAwcOsGjRIlq3bm19SG7ixImsXLmSvn37MmLECKpVq8b333/Phg0bGDFihMNqEbdr4sSJ9OvXj4ULFzJu3DjAXO5syJAhfPbZZ/k+qNelSxfatGnDuHHjOHPmDDVr1mTlypX88ssvLF682G5UObfjtmnThsaNG1O7dm0SExNZunQphw4dYtmyZXYVK0qVKsUHH3zAwIEDad++Pb179yY6OpqZM2fSsmVL6xxmW/v37+fw4cMMHz4819UCZ8+ezfr162nbti1VqlTh6tWrrFq1in379vHss8/SvHlzu77vvfce9erVo06dOjk+WWjevDnVqlXL85oVVolJkJVSrsBKoB0QobX+Lp9dij2tNfM2RfH9zwdpZjhJzSBf3uz3MO6u5oH9jCNHievdF331GoagIAJWrMC1Ws6HELIr6Pxh68hv1tzhgoz82n7sr91L5XwY8PqZQj0MeCfdqSWtC/sA4624lb8bIYQo7rp3787PP//MtGnTmDdvHomJiZQvX57GjRvzzjvvWPtNmDCB9PR0lixZwpo1awgNDWXSpEn4+PgwZMiQAp3Ly8uLkSNHsnnzZr7//nvS0tKoVKkSr776qt0IpL+/P7t27WLSpEl88cUXXL9+nWrVqjFjxgzGjs11Fudt6dWrF5MnT+aDDz5gxIgRhV5yWinFmjVreP311/nyyy+tc4i/+uor+vXrV6BjPPLII3zzzTfMnz8fDw8PWrRowUcffWStpWxrwIABuLm58e9//5tRo0bh5+fHCy+8wNtvv+1w/nZ+tY8BWrRowZ49e1iyZAlxcXG4u7tTv359PvnkE4YOHWrXd//+/QAcPXo0x+ImAJ999lmRJ8gqt9IcxYlSygAsBfoCz2utPynsMcLCwrRt4WtnM5k0/7chktW/mmeItA11Y2r/cNxczSPHGX/+SdwzvTFduYIhIICA1Stxq1mzyOMozDzX7A/jXZr4Fh9sOmH3MGBCUjqvdK7n9AfwiqKSR37Hv9OVOGQOshBCCHHHObx5l5QR5A8wJ8c7gBSlVP9s2zdprWPufli3xmjSTPvvYU78sQtfAnmicU0mdWmAS1aClREVRVzvvubkuFw5AlYuvyPJMRRugYPsH/ufOXG+wA8DFqWCjAwXRSWPvNyNShyy+IQQQgjhHCUlQX446z0865VdK6BEJMiZRhNvfXuIHYfO0FwlULNSEBO7NLAmeBnR0cT16oMpPh6Dnx8BK5bjdgdWiLkV2T/2r1z9AVxOnrirq+IVdGS4MJU8hBBCCCFslYgEWWvd0tkxFIX0TBOTV/3OjshYwJ0Kf/s7ozs1tCZxGSdOEterN6bYWFTZMvgvX4ZbvbrODdpG9uWl/dxL0ST0yh2di5tdQUeGZUlrIYQQQtyqEpEg3wtS041MXL6f9JO/UA0fWoU/wbOtqluT48xTp4jr1QtTzGVUmTIELF+Ge4P6dySW25nbavuxv4K7vipeQUeG78aDdEIIIYS4N0mCfBfcSMtk/Nf72f9XAg8qF5rXqchzrW/OLc08fdr8QN6lGJSvLwFfL8WtZk0yoqNzJLG3++BW9gft8qvbm9/57vaqeAUdGb5XlrQWQgghxN0nCfIdlpiSwbgvfuXPC1cBV9o++Xd6Natq3Z559ixxvfpgvHgR5eNDwNdf4VanjsMktrDJrSOFqa9bFOcraoUZGS7pS1oLIYQQwjkkQb6Drt5IZ/QXv+Ebs5e/qUxadX6Gp5vcXAEv8/x5c3J87hzK2xv/pV/i/vBDZERHO0xii2LxiMLU1y2qxSqKslyZjAwLIYQQ4k4rcUtNlxRxiWm8+PlvRF1K4pyqQJOwMLvk2HjhInG9emM8cwbl6Yn/l0so9bcwIPelm29lSWedmkpGdLR1qWLLg3Z+s2flOyJcFEtI34klky0jw+0aVKTeA2UkORZCiEKKjIxkwIAB1KhRAw8PDwIDAwkLC2Ps2LFcvHjR2eHdE5RSdi9vb2+aNGnC/PnzKYo1KH7//Xfat2+Pr68vZcuWpXv37pw8ebLA+x88eJCnn34aPz8/vLy8ePTRR/n+++9z9Lt48SKTJk2iffv2+Pv7o5TijTfecHjM7du38/zzz1O3bl28vb0JCQnh6aefZt++fQ77Zr9Gltfbb799y/EWFRlBvgNirqUw+rNfuH4lHleXMox9piUt65a3bjdeukRsr94Y/zqN8vDA/4sllGra1Lo9e7UISxKbW3tucpsiUdD6uoU9nyOyZHLJc6dWIBRCFA+7d++mVatWlC1bliFDhlCtWjViY2M5ePAgn3zyCV26dKFixYrODvOe0Lx5c1544QUALl++zNKlSxkxYgQxMTG8+eabt3zcyMhInnjiCYKCgnjnnXdITU1l5syZPPbYYxw4cIDy5cvnuf8ff/xBixYtKF26NOPHj8fHx4dly5bRuXNnVq9ebbd89LFjx5g2bRpVqlShSZMmbNq0KdfjTpw4kfPnz9OzZ0/q169PbGwsCxYs4JFHHuG///0vTz31VI59hg4dSsuWLe3aGjdufMvxFhmt9X3xatKkib4bzsbf0F3+b7t+duo8/fob/9I7Dp+x254ZE6MvPR6uzwWH6HPVquuUnT/dsVjSjx/XMR3/ruMGDtYxHf+u048fv2Pnyo0pJUXHPTfMHMdzw7QpJeWuxyAKzmg06Tk/RurBC3bpAR/9rAcv2KXn/BipjUaTs0MTQhSRTp06aW9vb3327Nkc2xITE/XVq1edEFXhJCcnOzuEfAE6IiLCri05OVlXrlxZly5dWmdmZt7ysbt27ap9fHz0uXPnrG0HDx7UBoNBjx49Ot/9//73v2sPDw996tQpa1tmZqZu3LixDgkJ0RkZGdb269ev69jYWK211sePH9eAnjp1qsPjbt++Pcf3FRMTo/39/XXDhg3t2rdt26YB/cknnxRpvLfAYd4oUyyK0F+xSbyw+FcuXU3ltGsoj7XrzBP1K1m3G+PiiOvVh8wTJ6BUKfwXf4rH44/dsXiKYorE7SrMlA7hfLZ1pgN9PfDzdrfWmRZC3BtOnDhBjRo1CHFwT/Dx8aFMmZsPNn/++ecopdiwYQMTJ04kODgYT09PnnjiCfbv359j/8zMTKZPn069evXw8PAgICCA/v37c+7cObt+P/30E/369SM0NNTar2fPnkRFReU4plKK/v378/333xMWFoaHhwfTp08HoGXLloSEhHDq1CmeeuopfH19KV++PJMnT0ZrTXx8PAMGDKBcuXKULl2aoUOHkpptqt/atWvp2rUrlSpVolSpUlSoUIHBgwdz6dIlu35//fUXSilef/11vvnmGxo2bIiHhwe1atVi5cqVBbr2np6ePPLII1y/fp3Lly/bbTtz5gyRkZH5HiMpKYn169fTs2dPHnjgAWt7w4YNadWqFcuXL8/3GDt37uSxxx6jatWq1jYXFxf69evHuXPn2LFjh7Xd19eXgICAAnx3EB4ejouLi11bUFAQ4eHhHD16NNf9bty4QVpaWpHEW1QkQS4ixy9dZ/TinymTeAKfUi7MGPQoHZo3sm43xscT17sPmcePg7s7/os+wSPc0aKARae4JKeWKR2SHBd/edWZFkIds0+pAAAaWElEQVTcG0JDQ/nzzz/56aefCrzPq6++yoYNG/jnP//JhAkTOHr0KK1ateL48ePWPlprnnnmGaZMmcLjjz/Ohx9+yIsvvsj69etp3rw58fHx1r4rV67kwoULDBo0iDlz5jB8+HB27tzJ448/TmxsbI7z79u3jwEDBtChQwfmzJlDs2bNrNtSUlJo27YtlSpV4r333qNRo0a8/fbbvPfee7Rr1w6TycQ777xDp06dWLx4MW+99ZbdsRcvXkxmZibDhw9n7ty5RERE8J///IdWrVo5TNp++OEHRo8eTa9evXj//fcpVaoUffv2dZjcO3Lq1CmUUvj5+dm1Dxw4kLp1818c7ODBg6Snp9PUZmqmRbNmzbh8+XKOX0iyS09Px8sr58JZ3t7eAOzduzffOArjwoULBAYGOtw2btw4fHx88PDwoFGjRg5/2bjb8QIyxaIoHD57Rbebtlk/M+VzPfmNt/TuwyfttmfGJ+hLbdqZp1VUCdXJGzfdsViEuB1Hzl3Vgxfs0mO/3KvHLd2nx365Vw/6aJc+cq74f+QqhCiYHTt2aFdXVw3ohx56SL/00kt66dKlOiYmJkffzz77TAO6WrVqOjEx0dr++++/a4PBoHv16mVtW7FihQb0unXr7I6xf/9+7eLiol977TVrW1JSUo5zHTt2TLu7u+tp06bZtQMa0Lt27cqxT3h4uAb0rFmzrG3p6em6YsWKWimlx40bZ9c/LCxMlytXzq7NUSzbt2/XgF62bJm17dSpUxrIMT3lwoUL2t3dXY8fPz5H3D179tSxsbE6NjZWHzlyRI8ZM0YDulu3brl+L/lZtWqVBvTatWtzbJs3b54G9O7du/M8xoMPPqgrVKiQY6pKz549NaBHjRrlcL/8plg4smnTJg3k+Lv43//+p7t06aIXLlyo165dq2fPnq1r1qypAT137twiibeAHOaNTk9c79brTiXIB/5K0K3e2aSbTvlBd5y+RR+Isp9zbExI0DHtnzQnx5Wr6uQffrgjcQhRFCxzkAd9ZJ6DPOgjmYMsxL3ot99+071799Zly5a1JqCurq561KhROj093drPkiC/++67OY7Rrl077ePjo41Go9Za627duumQkBBrQmj7qlmzpm7WrJnDWBITE3VcXJyOjY3VDRs21N27d7fbbknkHQkPD9cGg0GnZHu+pUuXLhrQUVFRdu0vvfSSBnRCQkKOY5lMJn3t2jVrzGXLlrVL6iwJct++fXPs++CDDzqM29GrX79+tzXP+4svvtCA/vHHH3Ns+/TTTzWgt23blucxPv74Yw3ozp076/379+uoqCg9ZcoU7e7urgE9dOhQh/sVNkE+e/asrlChgg4NDdXXrl3Lt/+NGzd0zZo1ta+vr75+/fptx1tADvNGqWJxG349Ec+Ur3+hgfE4F3zq8X9DmlI5wNu63XT1KnF9I8g4fBhcXCj30Xw8O3RwYsRC5E3qTAtxfwgLC2P58uVorTl27Bhbtmxh5syZzJkzh4CAAKZMmWLXv3bt2jmOUbt2bTZt2kRsbCzly5cnMjKSc+fO5fpRutFotP754sWLTJw4kXXr1nHlyhW7fo7mu4aGhub6vQQFBeGRbQpf2bJlAahcubLD9oSEBOsUh6ioKCZOnMimTZtISkqy6589NoAqVarkaPPz8yMhISFHe5s2bZg4cSJGo5EjR47wzjvvcPHiRdzd3XP9fvLj6ekJ4HD6h2V+taVPbp577jkuXrzItGnTWLduHQDBwcHMnj2b4cOH4+vre8vxWcTFxdG+fXsyMjLYsmULpUvnXNArOy8vL0aNGsXo0aP55ZdfaN++/V2LNztJkG/R/45dZtLKP/DITMfHJZ3JT9eyT46vXycuoj8Zhw6Zk+N5c/Hs1NGJEQtRMLICYeFIWTxRkimlqFOnDnXq1KFPnz5Ur16dJUuW5EiQLc8l5MVkMlGtWjUWLlzocLslaTOZTLRv357z588zduxY6tevj4+PDwaDgTFjxmAymXLd15HsD4UVZJvW5jrEiYmJhIeH4+bmxtSpU6lZsyZeXl4opejTp4/DWPI7pq0KFSrQtm1bADp06EBYWBjh4eG8/vrrzJgxI9e48xIcHAyY5/VmZ2mz9MnLlClTGDduHIcOHcLV1ZXGjRuzZcsWAGrVqnVLsVlcvXqV9u3bc+7cObZu3Uq9evUKvK/lF5C4uLi7Fq8jkiDfgi1HLvHm6gOkmwxUDAhiVP8OVPSzSY4TE4mLGEDG73+AwYDfnA/x7Jyz9p8QomQzmTTzN0ex71QCRq1xUYomoeV4sW0tSZJFiePv70/16tU5cuRIjm2OqiscO3YMHx8f64hxjRo12LlzJy1btsTVNff04tChQxw+fJjPPvuMwYMH221LSEgocMWEorB161YuXbrEtm3b7GrxpqSkOBw9vl1PPPEEPXr0YO7cuYwaNcquKkNBNWzYEDc3N/bs2cOwYcPstu3Zs4egoCCHFUoc8fHx4dFHH7V+vXHjRpRStGvXrtBxWSQlJdGxY0ciIyPZsGEDYWFhhdo/OjoaMH8ycDfizY1UsSik9b+f591Ve2ihD/BQmUQ+GvI3++Q4KYn4/gPJ2L8flMLvw1l4denixIiFEHeKlMUTJdHmzZvtpjtYnDx5kj///NNhJYVFixZx48YN69d//PEHW7ZsoWPHjhgM5lSib9++JCYm8t577+XYX2ttHRG0jMBmH51dsmTJXV/FL7dY3nvvPYejx0Vh0qRJpKen57hOBS3z5uvrS6dOnVi9erXd9Tp8+DDbtm2jV69ediP+BT3u0aNH+fjjj+nWrRs1bnFBr5SUFDp37sy+fftYvXo14XlU63I0JSUhIYFZs2bh5+dnlwjfqXjzIiPIhfDtb2eZ/t1RDLiT6RXA+J7NKedTyrrdlJxM/MBBpO/dC0pR9v9m4NW9mxMjFkLcSXmVxZMpKqK4GjNmDFevXqVLly40aNAAV1dXoqKiWLJkCenp6Q6X+fX19aV58+YMHjyY69evM2fOHLy8vPjXv/5l7dOvXz/WrFnDa6+9xq5du2jdujWenp6cOnWKNWvW0K9fP9544w3rlI6XX36Z06dPExwczJ49e1izZg3VqlW7m5eCFi1aEBgYyIABAxg1ahSlS5dm69at/Pbbb/j7+9+Rcz788MO0b9+exYsXM3nyZOuqhQMHDmTHjh0Op2pk9+6779K0aVOeeOIJRo0aRVpaGjNnziQwMJBJkybZ9XV03D179jB+/Hg6duxonT++cOFCgoODmT9/fo7zWX4mLEntzp07rW0DBgywTouIiIhg+/bt9OjRg4SEBJYuXWp3nG7dullLs/Xq1QsvLy/CwsIIDg7m9OnTLFq0iJiYGJYsWWLtdyvxFoncnt671163W8Xi659P6dZTvtWPTlmvn1u0WyempNttNyYn68s9njFXqwgO0Uk2pWGEEPcmKYsnSqIffvhBP//887p+/fq6bNmy2tXVVQcHB+sePXrkKKVmqWKxfv16/corr+gKFSroUqVK6ccee0z/9ttvOY5tNBr13Llz9cMPP6w9PT21j4+Prlu3rh4xYoQ+cuSItV90dLTu3Lmz9vPz0z4+Prpt27b6wIEDOjw8XIeHh9sdEwcr0lmEh4frBx54IEf7oEGDNJBjhbWpU6dqQB+3WVl23759ulWrVtrX11eXLVtWd+vWTZ88eVJXqVJFDxo0yNrPUsXCtlydbRyFiduyitzYsWPtjkEByrzZxt22bVvt7e2tS5curbt27Wr3feV13NOnT+tOnTrp8uXLazc3N12lShU9duxYh9U9LN9Lbi/bihlVqlTJs6/tSnizZ8/Wjz76qA4ICNCurq66XLlyulOnTg4rcBQ23kJymDcqXYDfVO4FYWFh+lYLSX+24wSfbo0kXP2O0bciU0cNxNP95uC7TkkhfvA/SPvf/wAoO/3fePePKJK4hRDFl2UO8t6TCZjQGFCEVZM5yOLe8fnnnzNkyBA2bdpkfdhMiHuMw/+sZYpFHrTWLNgSzZKfTgKuZFZ4kJd7NrdPjlNTiR/6rDU5LvPuO5IcC3GfkLJ4Qghxb5IEORdaa2b9EMnG3YcpjYGwetV5s8eDuLnefK5Rp6UR/9zzpO3YCUCZf72Fz6CBzgpZCOEEUhZPCCHuPZIgO2Ayad777ihr9p3lMXUKH29PXuvR1T45Tk8n4fnhpG3dBkCZqVPw+ccQZ4UshBBCCCGKiMxBzibTaOLtNYf54aC5dEqXBn6M6FCX0qVvrtKiMzJIGDac1B83AlB68mv4Dh9+ZwIXQgghhBB3isxBzk9Gpokp3xzk8NFIanCDsGYteOnJ2nb1BHVGBgkvjryZHL86UZJjIYQQQoh7iCTIWVIzjExa8Tu7jsdRT12lum8GL7aplnN5TYMBg4+5Np/v+H/iO3KEE6IVQgghhBB3iiTIQHJaJq8sO8C+U/GA4vFW7Yh4tBLu7u45+ioXF8rO+ACPjh3xbF/0SxsKIYQQQgjnuu8T5KTUDMYu3U/c2WgeVRdp0rozEU9Uz3MfZTBIciyEEEIIcY8y5N/l3nUtOZ2RS/Zy6OxVMpQrIUF+PNMs1NlhCSGEEEIIJ7pvR5Djk9IYvWQv5y/HY1ClGNHtcZ58sGLOOcdCCCGEEOK+cl8myJevpTLqi72kxZ3mCcMJHm7VhY6Ngp0dlhBCCCGEKAbuuwT5wpVkRi7Zy4UrKXi5lKNaHV86t2jg7LCEEEIIIUQxcV8lyGfibjBiyW+o6xfxcA1gekQYf6vm7+ywhBBCCCFEMXLfJMhpGSaGf/YrpqR4HjVE0ahZiCTHQgghhBAih/smQT4dfwPvpHRKe/rR/InOtGnW2NkhCSGEEEKIYui+SZDddToVPI28P7g5NSv4OjscIYQQQghRTN03dZA9VAaDG7pKciyEEEIIIfKktNbOjuGuUErFAqedHUchBABxzg6ihJFrVnhyzQpHrlfhyTUrPLlmhSPXq/Dkmt0Up7V+MnvjfZMglzRKqb1a6zBnx1GSyDUrPLlmhSPXq/DkmhWeXLPCketVeHLN8nffTLEQQgghhBCiICRBFkIIIYQQwoYkyMXXx84OoASSa1Z4cs0KR65X4ck1Kzy5ZoUj16vw5JrlQ+YgCyGEEEIIYUNGkIUQQgghhLAhCbIQQgghhBA2JEEWQgghhBDChiTIxYRSqolSapZS6qBSKlEpdUkptUUp1dbZsZUUSqnWSimd9arh7HiKK6VUsFLqY6XUOaVUWtb7N0qp0s6OrThSSlVWSn2qlDqllEpRSp1USi1USlVydmzOppTyUUq9oZRap5S6mPVv7/Nc+roqpSZnXcdUpVSkUmqkUkrd5bCdpqDXS+4HNxXmZyzbfvft/aCw10zuCY65OjsAYTUeaAN8A8wFfIAhwCal1Ita64+cGVxxp5RyB+YBNwBvJ4dTbCml6gA7gERgIXAeCAIeA7yA686LrvhRSvkDe4BSwHzgL6ABMAz4u1Kqvtb6mvMidLoAYCpwEdgLPJVH34+AZ4FPgF+B9sAcoBzw1p0Ns9go6PWS+8FNhfkZA+R+QCGumdwTcicJcvExGxistU61NCilPgJ+B95RSn2itc50WnTF38uYb7SfAGOcHEuxlDVStxQ4B4RrrZOcHFJJ0BuoAHTRWq+1NCqlTgGzMCd5q5wUW3FwEQjRWp9XSrkCGY46KaUaYU6OZ2qtx2U1L1JKrQImZf3/dvHuhOxUBbpeyP3AVkGvma37/X5Q0H+Xck/Ig0yxKCa01rts/zPMaksBvgP8MN+khQNKqSrA68BE4H4ezctPa6AJMFVrnaSU8lRKuTk7qGLO8hHjhWztlq9v3MVYih2tdZrW+nwBuvbOev8wW/uHmEfnuxZpYMVUQa+X3A9uKsTPGCD3AyjUNZN7Qh4kQS7+goFM4IqzAynGZgMHgc+dHEdx1yHr/YZSajeQDKQqpbYqpeo7Ma7ibGvW+xylVHOl1ANKqXbAO8BuYKPzQitRwoAYrfXpbO2/AibMN2mRP7kf5E/uBwUn94Q8SIJcjCml6gHdgbVa6/t6pCo3SqmnMM+vGqll1Zv81Mp6X4n5I7VngHHAg8BOpdQDzgqsuNJa/wqMAOoAP2O+bhuBY0C7++hj7tsVjHluox2tdToQD8jPXj7kfpA/uR8UmtwT8iBzkIuprKdHV2H+jW6sk8MplpRSnphHCxZprfc5O54SwCfr/YDWuqelUSm1F/gf5nl74xzteJ+7wM3R4hOYbx7jgXVKqU5ZH32LvHmS+8M+qVnbRS7kfpA/uR/cErkn5EES5GIo6x/6OqAa8KTW+oyTQyquXgPKZr2L/FkSua9sG7XWPyul/gLC73pExZxSqjuwAmistT6S1bxWKbUf+B4YDsx0VnwlSArmucaOeHDzZ1NkI/eDApP7QeHJPSEPMsWimMkqT/Mt8CjwjNZ6h5NDKpaUUsHAP4GPgbJKqRpZtS7LZXWprJQKdVqAxZPlwbJLDrbFYH74R9h7CThukxxbbMA8mvfE3Q+pRLqAeZqFnaz/7/zJ+RCkQO4HBSX3g1sm94Q8SIJcjGSVY1kJtAMGaq2/c3JIxVkQ5hGpCcBxm9eorO1bgAPOCa3Y+i3rPcTBthAg9i7GUlJUAFwctCvM/3/KE98Fsw+ooJSqnK39b5ivo3wkno3cDwpF7ge3Ru4JeZAEuZhQShmAL4AuwHCt9XInh1TcncL8QEH2l6Um7ShgoHNCK7b+i/kjtaFKKWvSp5TqhPkhqR+dFVgxFgnUVEo1zdbeC/PUgL13P6QSaWXW++hs7aOBdGDN3Q2neJP7QaHJ/eDWyD0hDzIHufj4AOiLeUWbFKVU/2zbN2mtY+5+WMVT1uplq7O3K6UaZP3xB6119N2NqnjTWscqpSZj/lnbmrVIQzDmaQSnkLm0jkwHOmJewWw+cBLzQ3rPYy7GP9+JsRULSqmRmOd+WgZcHlRKvZ7157Va64Na6wNKqcXAOKWULzdX0usFvKm1vm+mWBTkeiH3AzsFvGZyP7BRwH+Xck/IgyTIxcfDWe/hOJ4Y3wrznCAhbpnWeoZSKh7zk/AfYF5edBXwqtZaaqtmo7XepZQKA6ZgTlgqYi5LtgyYrLW+7Mz4iol/AlVsvn4o6wXm0lEHs/48HDiDecnkwZiX7X4J83LT95OCXC+5H9gr6M+YuKlA10zuCblTUipQCCGEEEKIm2QOshBCCCGEEDYkQRZCCCGEEMKGJMhCCCGEEELYkARZCCGEEEIIG5IgCyGEEEIIYUMSZCGEEEIIIWxIgiyEEEIIIYQNSZCFEOIeo5TarpTanq1NK6XeKOLz/KWU+rwojymEEMWBJMhCCFFElFItsxJRy8uklEpQSv2olGrp7PhuhVKqe1En1kIIUdzJUtNCCFH0PgW2Ay5ADczLLG9WSrXXWm91UkyeQOYt7NcdiADecLCtNmC6jZiEEKJYkgRZCCGK3m6t9VLLF0qp/wD7gfGAwwRZKeWltU6+UwFprVPvwDHTivqYQghRHMgUCyGEuMO01geAeKA6WOfu/k8p1UwptVMplQzMt/RXSoUqpb5QSsUopdKUUseUUuOVUnb/ZyulXJVSbyulziulkpVSPyulmjmKwdEc5Kz9X1ZK/aGUSsmaDvKTUqpL1vbtmEePLftbXlVtvo/Psx2zlFLqX0qpE1mxn1VKzVZKlcnW742sYzVUSs1USl3O+h42KKWqFPoiCyFEEZIRZCGEuMOUUv6AHxBl0xwCfA98AXwJXMvqWwP4BUgC5gCxQEvgPaAqMMLmGB8BzwLfAT8AdYENQAJwNp+YDMA3wNNZ+34OaCAM6AD8F3gHcAOaAwNsdo/N49CrgaeAVcAMoBEwEmihlGruYNR5MXAFeAuoCIwDlgKP5xW/EELcSZIgCyFE0fNRSgVg/pSuBjAt68/LbPpUAfpqrZdn23c2kAg01lpfz2pbqJS6CIxRSs3SWh9XStXHnByv1Fr3tuyslDqCeTQ6zwQZ6Ic5Of5Aaz3edoNSSgForTcppQYBzW2njORGKdUJc3I8V2s9yqb9KDALeA6Ym223c1rrbjZ944EZSql6Wuuj+Z1TCCHuBJliIYQQRW8m5lHWGOBnoAnm0Vjb5DAeWGm7k1KqLPAk5pFdd6VUgOUF/AgooHVW985Z77OynftTskaj89ELSAXezL5Ba60LsL8jT2e9T8/WvgC4brPd1kfZvt6W9V79FmMQQojbJiPIQghR9P4P81QHE+Zk9YiDh+ROa62zV4CohTkJ/mfWy5GgrPeqWe/HbDdqrdOVUqcKEGNN4KTWOqkAfQuqKpCktT6XLaY0pdRJINTBPqezfX0l671cEcYlhBCFIgmyEEIUvT+11pvz6ZPioM3yqd4CzKPIjpzMeldZ77c62nu7+xb2mCqXbcY8+gshhFNIgiyEEMXHCcxJpC5Agm0ZJa6D+aE+AJRS7phHan/PZ//jQFullLfW+kYe/QqTRP8FdFBKhdiOItvEtKcQxxJCCKeROchCCFFMaK1jgS3AoKxqFnaUUqWVUqWyvvwu631Mtm5DgTLkbyXmxUMmOziP7ehtUlabXwGOuS7rfXy29mFAaZvtQghRrMkIshBCFC8vYH6w74BSahHwJ+aEtwHQI+v9L631YaXUZ8AQpZQX5jnP9TDXLT7p8Mj2vgb6AhOUUg8CGzFPd2gCJAMvZvX7DfNKgHOVUhswr8a3LpdR5/WYS9eNVkqVB3YCDYHnMS+U8nFhLoQQQjiLJMhCCFGMaK2jlVIPA69jTogrYH5wLQpzxYlLNt2HZX09BGgLHAA6Av8uwHlMSqmuwMvAQMyVJ5KAw8AHNl2/xJw098ScUCvM0yVyJMhaa62U6pEVe3/My1THYi47N1lW3hNClBTq1qv5CCGEEEIIce+ROchCCCGEEELYkARZCCGEEEIIG5IgCyGEEEIIYUMSZCGEEEIIIWxIgiyEEEIIIYQNSZCFEEIIIYSwIQmyEEIIIYQQNiRBFkIIIYQQwoYkyEIIIYQQQtj4fzhZsSKks151AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot training loss (based on the best mae among the three traced models)\n",
    "import matplotlib.pyplot as plt\n",
    "from xenonpy.model.training import Trainer\n",
    "from xenonpy.model.training.dataset import ArrayDataset\n",
    "from torch.utils.data import DataLoader\n",
    "from xenonpy.model.training.extension import TensorConverter\n",
    "\n",
    "train_batch_size = 512 # based on total number data\n",
    "test_batch_size = 1024 # based on total number data\n",
    "\n",
    "for i, checker in enumerate(checkers):\n",
    "    # plot training loss\n",
    "    fig, ax = plt.subplots(figsize=(10, 5), dpi=150)\n",
    "    trainer = Trainer.load(from_=checker).extend(TensorConverter())\n",
    "    _ = trainer.training_info.plot(y=['train_mse_loss', 'val_mse'], ax=ax)\n",
    "    fig.savefig(f'{save_plots}/Train_{model_list[i]}')\n",
    "    \n",
    "    # plot observation vs. prediction (based on the best mae among the three traced models)\n",
    "    sp = checker['splitter']\n",
    "    x_train, x_val, y_train, y_val = sp.split(desc, prop)\n",
    "    \n",
    "    x_colnames = checker['x_colnames']\n",
    "    x_train = x_train[x_colnames]\n",
    "    x_val = x_val[x_colnames]\n",
    "    \n",
    "    train_dataset = DataLoader(ArrayDataset(x_train, y_train), shuffle=True, batch_size=train_batch_size)\n",
    "    val_dataset = DataLoader(ArrayDataset(x_val, y_val), batch_size=test_batch_size)\n",
    "    \n",
    "    y_pred, y_true = trainer.predict(dataset=val_dataset, checkpoint='mae_1')\n",
    "    y_fit_pred, y_fit_true = trainer.predict(dataset=train_dataset, checkpoint='mae_1')\n",
    "    draw(y_true, y_pred, y_fit_true, y_fit_pred, prop_name='HL gap',file_dir=save_plots, file_name='P2O_'+model_list[i])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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